The System Design Process


Design Process Design Space Multiobjective Optimization Design Methodology Design Team 
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Everyone takes the limits of his own vision for the limits of the world.

Arthur Schopenhauer, 1788–1860. 15 1

Research in microelectronic integration for wireless LANs is obviously the right way to go to obtain low-cost low-power solutions that meet business expectations for a consumer market. So, why not start designing them ad hoc, right now?

Maybe because a few questions remain, such as do we have a clear specification of what we want to obtain as a result? Do we know how we get from our expectations toward a prototype implementation or a final product? Can we repeat this process? 16 2 And finally, what does design actually mean?

Designing 17 3 as a human activity is not easily captured in a single definition, but a combination of creating or executing in a highly skilled manner, formulating a plan or devise something, and having something as goal or purpose [AHD00] seems to bring together all important aspects: each design needs a clear goal to achieve; it requires the concept of a method, path, or process that leads to this goal; and it relies on particular skills to reach the goal while staying on the intended path.

A particular design activity relies on a requirements specification as input on what to achieve and is basically given freedom in how to achieve it. Obviously, the most efficient process of achieving the specification is desired. Traditionally, requirements for an electronic system could be rather easily and early partitioned into a few discrete disciplines such as analog and digital, mechanical and electronic, low-power and high-power parts, etc. Little margins on cost, power consumption, and performance for portable, consumer-oriented devices such as WLAN do not allow such an upfront partitioning; instead, a multiobjective system-level optimization is preferred. The definition of a system 18 4 directly explains that codesign and optimization at a larger scope, taking into account more components together, offer a potential benefit in better functionality or lower cost. However, it also complicates the design process since the design space has been enlarged. Fig. 2.1 illustrates the difference between vertical intratechnology and horizontal intertechnology exploration. The figure is an extension of Kienhuis' abstraction pyramid [Kienhuis99].
Fig. 2.1.

Extension of the abstraction pyramid to a multisegmented abstraction circle which captures both intratechnology (vertical) and intertechnology (horizontal) exploration. Horizontal exploration within one technology may start a trajectory of changes across multiple technologies to meet overall specifications

If we assume that the same design cost can be spent, then we might reduce design cost along the vertical design space exploration axis and shift it to the horizontal exploration axis. First, this requires increased efficiency in design refinement and synthesis within a particular technology or discipline. During the last years, progress has been made on this aspect. Second, horizontal exploration requires understanding and interaction between traditionally different skills, hence interdisciplinarity. Kienhuis [Kienhuis99] stated that an increase in abstraction level leads to lower exploration cost. However, he assumed the availability of efficient and sufficiently accurate behavioral models. This assumption neither does hold for analog and microwave/RF nor will be applicable for digital designs processed in nanotechnology. Also, intertechnology exploration leads to heterogeneous modeling approaches, which largely increase the complexity of horizontal design space exploration. Colwell [Colwell04] stated this as “there's always cost in tying conceptually separate functions together.” But, is there an alternative and are these functions actually conceptually separate?

This chapter is organized as follows. Section 2.1 describes design as a process. We introduce terminology and the scope of design methodologies and design technology. Section 2.2 focuses on microelectronic system design. Requirements and common approaches are described that allow the identification of areas of mature design technology and methodology gaps. Finally, we synthesize our particular design approach for WLAN. Section 2.3 analyzes the consequences of such a design flow based on different levels of crossdisciplinarity. Codesign of design technology and application is addressed in particular. Section 2.4 summarizes our findings and directs the reader to the implications of our design rationale in Chaps. 4–7.

2.1 2.1 Design

Both artists and engineers rely on creativity, rooted in knowledge, and craftsmanship, as means for innovation. In principal, they both start with a blank page and a few requirements. However, in contrast to the fine arts, design in the engineering context has become far more constrained a priori by nontechnical requirements and cost aspects [Rissone02]. The amount of constraints allows a distinction between two different design goals: the design of a new product and the design of a derivative of an existing product. The first refers to the blank page situation in which creativity and exploration is central, the latter expects a quick transition from a changed specification to an upgraded product.

Hence, the purpose of engineering design is the translation of initial requirements into a product or object with these desired properties in a predictable, efficient manner. This identifies the need to describe design as a process [Jacome96]. In contrast to the era of the renaissance engineer, where the limited amount of knowledge still allowed a single engineer to be aware of all engineering aspects, design requirements and design processes have become so complex and diverse that engineering is only viable in a team context today. A division of engineering into a large number of specializations and subdisciplines has been the result. Consequently, it becomes more and more difficult for each engineer to identify his or her role in the design process. Managing and enabling smooth transitions of design information between engineers have become the major challenge. 19 5 Lately, the immersion of application and service aspects into the design process, for example the interaction between service cost and service quality, has forced a shift from a product- and hence object-centric approach to a goal-centric approach [vanLamsweerde03]. An example is the embedding of sophisticated control into devices to meet user-specified conflicting goals such as long operation time or high-quality content in a portable. This adds environment and content awareness to the design process, making it even more complex.

We will now try to understand the design process in more detail. First, we introduce a clear terminology and structure of the design process. Next, we investigate its development, deployment, and use.

2.1.1 2.1.1 Design as a Process

An indisputable fact, design represents a process and shall lead to a particular goal. It is a philosophical question whether we embed the desired characteristics of the process, such as design time or design cost, in the goal or whether we establish them separately as properties of the process. Design philosophy may include abstract and hardly quantifiable requirements for the design process such as 20 6:
  • Maximize the probability of success

  • Develop all critical design support in-house

  • Use only advanced, stable, and proven CAD tools

  • Identify major risk factors first

Clearly, this is input to but can hardly be the subject of an engineering thesis. Instead, we focus at a design process that leads us from an initial specification S[0] to a final specification S[m] through a number m of discrete design review steps. We expect from this process that it enables a design team to maximally reach the design goal, i.e., it optimizes the use of capabilities given the initial requirements and constraints.

Fig. 2.2 outlines the design process in a generic way. Starting from general design principles, we first have to come to a clear initial requirements specification. This specification includes desired and undesired properties as well as constraints. The result is a definition of the maximum design space in which a solution for the particular design has to be found. The properties of the requirements and the design space are input to the choice of a particular design methodology. The combination of methodology and design technology, i.e., computer-aided design tools that support the designer, allows establishing a design flow. This flow describes how methodology and tools are applied in the actual design, which is the last step in the design process. Note that we will first analyze this ideal top-down process. Practical issues are considered in Sect. 2.1.2.
Fig. 2.2.

The design process structures a design from the hardly quantifiable design philosophy that specifies the goals down to the actual design result Requirements Specification

The capturing of initial requirements in a requirements specification is a crucial but often underestimated point in practice. A significant amount of project or product failures can be traced back to an insufficient specification [Bell76]. The most important reasons for this are:
  • Positive requirements are more difficult to estimate than constraints [PageJ88]; this is obvious since constraints assume a preselection of technology from which then concrete data are available. Definition of requirements requires much more creativity.

  • Elicitation of primary objectives is difficult given the complexity and heterogeneity of current products [vanLamsweerde03]; conflicting objectives have become the rule, not an exception.

  • A requirements specification shall establish precisely what a product must do without describing how to do it. Unfortunately, distinguishing what from how is a dilemma in itself [Davis88]. The distinction is necessarily a function of perspective. Hence, requirements differ with the degree of abstraction and as the point of view changes, for example, from the designer to the user of a product.

Several approaches have emerged to deal with this problem, mainly instantiated in the field of software engineering, but largely applicable to system design in general. This ranges from general guidelines such as structured design [PageJ88], guidelines for requirements specification documents [IEEE830, IEEE1233], the unified modeling language to capture multiparadigm specifications [UML, Rumbaugh98], or the KAOS 21 7 approach in software engineering and support for automated reasoning [vanLamsweerde03].

Essentially, all these approaches treat design as an object-oriented process with a particular object instantiation as the endpoint of design. Hence, the design process is focused on managing the what-aspect. Recently, a goal-oriented view has been introduced [vanLamsweerde03]. This approach tries to embed the why-decisions in a design process. 22 8 This allows tracing back decisions in a design process.

With this initial notion of requirements specification, we can reformulate our definition of a design process as a process of deriving a complete specification, i.e., a set of properties that uniquely characterize a particular design object, starting from an initial requirements specification [Jacome96]. Design Space

The initial requirements specification implicitly defines the initial design space. Depending on the viewpoint and abstraction level at which the requirements are captured, the application design space is divided into a problem design space and a solution design space. The problem design space starts from the problem (goal) and encapsulates all possible solutions to the problem without constraints. The solution design space is a subspace of the problem design space with particular solution constraints being imposed (e.g., in terms of bounds on cost, latency, etc.). We do not focus on the problem design space in detail. Hence, from now on, the short-term design space denotes the solution design space.

Essentially, the design process aims at identifying the optimal configuration of the solution design space that meets all specifications. The optimal configuration represents a collection of entities that, based on their configuration, perform a specific set of tasks. This equals the definition of a system [Papalambros00]. Hence, the derivation of the complete specification or configuration in the design process completely defines the system.

Essentially, this represents a decision-oriented process that falls into an exploration/ optimization and a mapping/refinement step. The first allows traversal and comparison of different configurations in the design space based on requirement metrics. The second narrows down the design space based on a design decision. A design process that includes an evaluation criterion is a decision-making process. The result is an arbitrarily complex, partially ordered, sequence of generation design steps (mapping and refinement) interleaved with test and validation steps (exploration and optimization). Design Methodology, Design Technology, and Computer-Aided Design

This definition of the design process is neither constructive nor predictive in terms of cost. Hence, we need a methodology 23 9 that establishes a quantitative and qualitative capturing of the progress in the design process, a clear sequence of preferably finite design steps of predictable complexity. Moreover, the effort in implementing and learning the methodology should result in, e.g., better, faster, or more consistent design results compared to an ad hoc approach. Essentially, a methodology must go beyond the description of a single method: it has to be general enough to be applicable in a significantly large domain, i.e., its deployment and applicability must have been evaluated. 24 10

Efficiency and reproducibility advocate the use of computerized automation techniques for those design steps that have been mathematically formalized and do not require human reasoning. This results in design technology 25 11 or tools. The degree of automation allows a differentiation between electronic design automation (EDA) and computer-aided design (CAD) tools. In practice, no formal distinction is applied but CAD emphasizes that technology only supports the designer while EDA aims at full automation of design steps. A design methodology embraces typically both techniques. The application of design tools to accommodate a particular sequencing of design steps is called a design flow or tool flow.

2.1.2 2.1.2 Application and Rationale of the Design Process

Fig. 2.2 illustrates that the actual design process has a much wider scope than the actual application design, which is perceived by the designer as the design task. The design process includes both selection and/or development of design methodology and technology and its deployment toward the application designer. For a successful deployment and use of design methodology, it is important to place this theoretical idea of a design process in a real designer's environment. Applicability and acceptance by the designer are the essential proof for the rationale and success of a methodology. Fig. 2.3 illustrates this transfer of methodology through the three phases of development/definition, deployment, and use. These three aspects are addressed individually.
Fig. 2.3.

Design methodology is developed in a metaprocess called method engineering and essentially consists of the three phases of development, deployment, and use Methodology Development

As we have illustrated, the development of a methodology is itself part of the design process. This partial development, usually called method engineering, is itself driven by another process. Kumar and Welke [Kumar92] defines method engineering as a method for designing and implementing domain-specific methods. Hence, method engineering represents a so-called metamethod 26 12 with the goal of instantiating a general metaconcept into an explicit and concrete domain-specific methodology. The derivation from the generic to a particular instantiated concept is achieved through the application of additional constraints and requirements. Practical examples are the limitation to tools from a particular CAD vendor, the usage of a particular software coding style, or the experience of the design teams.

These examples show that methodology development does not necessarily result in writing your own tools. Instead, it consists of methodology selection, optional method construction, and tool selection (Fig. 2.3). Depending on requirements, an entire design methodology can be acquired from a CAD vendor along with the corresponding tools. An example is a register transfer level (RTL)-based design flow for synchronous digital designs starting from VHDL and resulting in gate-level netlists.

In general, however, design methodologies require adaptation to local requirements since design teams can rarely find a perfect match based on existing methods and tools [Tolvanen98]. This process is called local method development. We can distinguish between the development of new methods and the adoption of existing methods to differing constraints or assumptions. Tolvanen [Tolvanen98] classifies the method selection into three principles:
  1. 1.

    The textbook approach is driven by technical rationality [Schön83], i.e., the idea of reuse. This assumes that similar problems can be approached with a proven solution. However, this often excludes a formal assessment of similarity. Quite often, a number of potential techniques are selected and tried out in an ad hoc manner: this is essentially a trial-and-error method.

  2. 2.

    The contingency approach tries to classify problems into domains and reuse again domain-specific solutions [Kumar92]. The approach may result in suboptimal results due to the low granularity in classification but delivers fast results due to maximum reuse.

  3. 3.

    The method engineering approach requires an exploration and optimization strategy and hence a domain-specific development of techniques. Although a costly approach due to the method development cost, it may be the only technique to find the optimal solution in a complex constrained design space.


Obviously, these approaches can be combined at different levels of decision. Deployment of a Design Process

Design methodology is supposed to enable and support the application designer, but is rarely developed by the application designer. Current approaches for method selection and development actually do not provide adequate support for learning and creation of methodical knowledge. Although local (in-house) methods develop over time and methods must be seen as one part of the organizational knowledge, this knowledge is rarely captured in an adequate way [Tolvanen98]. This gap reveals:
  • An unawareness of what the in-house design methodology embraces (due to a lack of global view on the design process)

  • A hesitation to adopt new, disruptive design methodologies and tools

  • A resistance in formalizing the in-house methodology due to cost or effort

This prohibits a fast transfer or modification of the design methodology, e.g., in case of the integration of new team members or the partial change of the design flow. It also prevents the export of design methodology to third parties.

[Ciborra94] and Stolterman [Stoltermann92] have described that the case of transferring skills from a skilled designer to a less proficient one fails often due to a lack in formalization and capability of transferring know-how into methodology.

Stolterman [Stoltermann92] linked the capability of adopting disruptive new techniques to the capability of an organization for radical learning. Typically, individual designers prefer an incremental way of learning. Hence, adoption of new, disruptive methodologies cannot be delegated to individuals but requires the support of the organization to bridge development, transfer, and use phase.

In practice, new methodologies are often introduced through pilot projects in which a number of designers are trained and apply the methodology to a lightweight design project. This early feedback often allows a priori corrections and adaptations of the methodology before actual use in large projects. Note, however, that this assumes that the methodology scales adequately with the design size. This is not evident for complex system designs. The Design Process in Use

Once the design process is used by designers, the practical application gives rise to adaptations. We can distinguish four levels of feedback (Fig. 2.3). Ranking in severity from low to high, e.g., missing coding style can be locally supported through additional clarification or training. On the contrary, missing functionality may require the overall adaptation of the design flow and involve the acquisition of different or additional tools from external sources. Only local, domain-specific know-how will allow the appropriate selection of the feedback mechanism and hence minimize the cost of adaptation of the design process.

Ideally, the initial specification should never be modified. However, limitations in the accuracy of technological estimates (e.g., updating performance/cost models when more accurate information becomes available) or nontechnical constraints (e.g., fulfilling the time-to-market constraint that may vary due to changing market interests or competition) frequently require iterative adaptations of the initial specification [Jacome96]. It is essential though to avoid changes that require fundamental changes in the design process and the underlying technology. Consequently, for the technological side, it is important to identify less trustworthy estimates as early as possible in a risk assessment step and investigate them prior to other parts. Rationale and Conclusions

Referring to the need of a design process, we cannot end this analysis without addressing a measure for its success. Although need and success seem to be cornerstones of a design rationale, they only form part of it, mainly the reasoning component. A rationale must be seen as the sum of at least three different forms of knowledge: reason, aesthetics, and ethics. 27 13

Aesthetics denotes the ability to judge whether the process as a whole is considered appropriate, i.e., a good design. Stolterman [Stoltermann92] proposes the confrontation of the process with the designer in practice to answer this question. Interestingly, his studies revealed a so-called hidden rationality of practice, which appears familiar from a phenomenological point of view but deserves a deeper analysis to allow conclusions. This hidden rationality of practice is constituted by two apparent contradictions:
  • A fundamental mistrust in new methodologies

    Methods that show a simple and consistent picture of the design process appear irrational to designers. Designers evaluate their intrinsic structure and come very quickly to the conclusion that a method is not realistic enough. The designers' own complex and disparate view of the process appear to them a more attractive and pragmatic approach. Again, the fact that they cannot describe their own approach in engineering terms makes it acceptable; basically, the missing structure prevents a deep analysis and hence prevents early rejection based on facts. The result is often a fundamental, initial mistrust with new, unfamiliar methodologies.

  • A dilemma between art and engineering

    In fact, designers have difficulties in identifying the crucial skills that characterize good design; often the only answer is through experience [Stoltermann92]. Thus, system design appears sometimes as an art. System designers themselves and even more those who develop methods would, however, if they could choose, like to change the process to be like an engineering process. This either results in uncompleted approaches to formalize design steps or the early denial of any substantial generalization and documentation of how a design process looked like.

  • Design granularity: Creativity vs. problem solving

    Commonly, design is seen as a sequence of problem-solving steps that fix a malfunctioning reality. This impression often results from a too narrow insight of the designer toward the design process. Indeed, when assigned a small task with strict requirements without knowing why and how these requirements were derived, the designer's reaction becomes understandable. The small granularity prevents the exploitation of the designer's creativity. On the contrary, some designers may want to remain focused in their particular field of knowledge and feel overloaded by too much context. These opposing cases prevent an efficient information exchange within a design team when not addressed particularly.

Finally, we want to summarize a number of critical points on which a candidate design process needs to be evaluated. They form cornerstones of our design rationale:
  • An encapsulation of the entire design from requirements specification over methodology development to the actual design is obtained.

  • Effort invested in the requirements specification pays off in reduced iterations over the specifications and leads to a more predictable process.

  • Methodology is conceived as a whole from development over transfer to the actual use. The importance of local method development is crucial to the successful use of the methodology by designers.

  • Developing design methodology is a metaprocess that requires the instantiation of a generic idea into a specific flow on the one hand and on the other hand the generalization of a particular design solution into a reusable method.

  • Design has traditionally an object-oriented focus with the product as center. Newer concepts favor a goal-oriented approach that enables a traceback of design decisions.

We will come back to the rationale behind these points frequently in the following chapters, in particular in Chap. 7.

2.2 2.2 Microelectronic System Design

The goal of electronic system design is to minimize production cost, development time, and cost subject to constraints on performance and functionality of the system [Ferrari99].

Progress in IC fabrication technologies has enabled integration of complete electronic systems on a chip (SoC) or in a package (SiP) [Shen02]. This evolution toward embedded solutions is driven by the size and production cost advantage of integrated solutions and has surpassed the previous PC era as an industrial driver [Schaumont01a]. For example, in our particular case, we consider the complete integration of a wireless LAN transceiver — analog, RF, and digital, hardware and software, discrete and integrated components — using an advanced packaging technology with integrated multichip modules (MCMs) [Donnay00]. This move from general-purpose components to heterogeneous application- or domain-specific systems has been recognized by MEDEA [MEDEA02] and ITRS [Edenfeld04], which both introduced system-specific drivers, for example for wireless communications.

Hence, electronic system design moved from a board-level to a chip- or package-level approach. This also means that the task of designing and partitioning the system has moved from selecting discrete components and connecting them on a board into the microelectronics design domain. We refer to this approach as microelectronic system design. Characteristics of microelectronic system design are heterogeneous integration and performance/cost awareness.

This section is structured as follows. First, we state the challenges in microelectronic system design that come along with complexity and heterogeneity. Next, we analyze the key methodologies that have been proposed for this design domain. Finally, we synthesize a specific design methodology for our wireless LAN design case.

2.2.1 2.2.1 The Challenge of Complexity and Heterogeneity

Difficulties in merging microelectronics with system design have first been revealed in digital design. Digital design so far could benefit from technology scaling according to Moore's law [Moore65]. The result was higher performance at reduced chip size and reduced cost per function. This paved the way for the integration of more functionality onto a single chip. However, it is today clearly recognized (and addressed in detail in Chap. 6) that performance comes at a power cost, maximum performance is not always required, and hence performance can be traded against energy or peak power. Besides this performance/cost tradeoff aspect, we see that this evolution reveals the four major problems that even gain in importance when moving to a truly heterogeneous microelectronic system. Technology vs. Design Productivity Gap

The MEDEA roadmaps consistently referred in 1999 [Borel99], in 2002 [MEDEA02], and again in 2005 [MEDEA05] to the increasing gap between the available logic transistors on a chip and the capability of designers to make use of them in application design (Fig. 2.4). Due to this gap, the importance of design chains has become as important, if not more, than technology innovation since a lack in design efficiency prevents the full exploitation of a technology advantage and leads to a delay in time to market. Obviously, current EDA support is not sufficient. We can think of two reasons for this fact: either the capabilities of design automation tools were too limited or they were not adequately embedded in the actual design process.
Fig. 2.4.

The technology vs. design productivity gap has been increasing for a long time. Disruptive design methodologies are needed to close the gap Delays and Costs Due to Complexity-Induced Functional Errors

Advancing technology allows the integration of increasing functionality onto a single chip. However, failures in SoC designs are causing a significant number of redesigns: 48% of the designs fail on first silicon, 20% still on second spin, and 5% require even more than three spins. 28 14 Failing on first-time-right silicon may lead to missing the market window and hence to a significant loss of profit. Importantly, a large number of these designs actually failed due to functional or specification errors. This reveals a test and verification gap. The yield reduces with increasing heterogeneity in the same design, such as analog and digital on a SoC or discrete and integrated RF components in a SiP. With 20% of mixed-signal SoCs today and a projection of 70% for 2006, 29 15 the severity of this problem rises dramatically. The urgency for design support in the integration of heterogeneous components is also expressed in the ITRS 2003 roadmap [Edenfeld04]. Lacking Support for Power- and Energy-Aware Design

Unfortunately, performance and power consumption in Moore's law scale at the same rate. As a consequence, thermal dissipation problems add tremendous design complexity and slow down the usage of the available chip area. Both ITRS and MEDEA identify low-power design and system power management as key challenges. An Incoherent Marketplace for System-Level Design EDA

In 1993, the EDA industry introduced electronic system-level (ESL) design automation solutions for the first time at a large scale [Goering03]. The very diverse approach with many niche solutions such as graphical code generation, HW/SW codesign, architectural modeling, behavioral synthesis, etc., resulted in fading interest soon. ESL design obtained a bad reputation from that moment on. Around 2000, with the advent of SystemC, a similar movement started with significant EDA industry support to address high-level system modeling in C-based language styles. At the beginning of 2004, a diversification over several approaches and even a withdrawal of major EDA players from the ESL market can be observed. 30 16 Since then, we have mainly seen a HW/SW digital-driven initiative. ESL design tools for truly heterogeneous systems including analog/RF, power management, sensor/actuator support, etc., appear in 2006 to be far away from the designer's toolbox.

2.2.2 2.2.2 State of the Art in Electronic System-Level Design

Techniques for ESL design have to overcome the four problems aforementioned. For an understanding of the state of the art, we first have to introduce the concepts of function, architecture, and interface which allow the principles of hierarchy and abstraction.

A system implements a set of functions. A function is an abstract view of the behavior of the system. It is the input/output characterization of the system with respect to its environment. It has no notion of implementation associated to it. Contrary to this, we can describe an architecture that by itself has only a notion of implementation but no particular functionality. The notion of function and architecture depend on the level of abstraction. Higher abstraction levels require a partitioning of the design into visible and hidden or, equivalently, detailed and nondetailed aspects. This introduces general design principles such as hierarchical composition and the concept of interfacing to describe the interaction with a component whose content is not detailed, a so-called black-box component model. A Chain of Dependencies

Besides the automation of particular design refinement tasks through, for example, automatic synthesis techniques, we can order existing methodology trends into a sequence of depending methodologies with decreasing complexity:
  1. 1.

    multiobjective optimization to take into account concurrent constraints on performance, power, cost, etc., which requires

  2. 2.

    horizontal design space exploration and codesign techniques to compare and validate heterogeneous design solutions; they rely on

  3. 3.

    behavioral and gray-box modeling for an efficient comparison at a manageable level of detail; this calls for

  4. 4.

    design capture, verification, and reuse at higher abstraction levels to speedup the design and improve design robustness


Obviously, the least complex tasks have been addressed first in time and have, so far, been most successful. An example is methodologies for the move of digital design to higher abstraction levels. For verification purposes at design time, a so-called silicon virtual prototype (SVP) is built. Over time, the abstraction level of this prototype has moved from the classical layout, transistor, gate level to the register transfer level [Mehrotra03, Dai03] and finally to the transaction level 31 17 [Gordon02, Schlebusch03]. Preferably, a SVP is prepared at all levels but verification of system-level features is achieved most quickly at the highest abstraction level. The design cost reduction due to reusing existing components, the so-called IP, 32 18 increases also with the abstraction level.

Further abstraction is possible through platform- and interface-based design techniques. The Y-chart approach combines separate functional and architectural specifications through a formal mapping step into an implementation [Gajski83, Ferrari99, Kienhuis99]. The separate architecture specification allows the mapping of different functionality to the same architecture, which is denoted as platform-based design [Keutzer00]. A formal separation of data manipulation into communication/transfer and computation/behavior constitutes interface-based design [VanRompaey96, Rowson97, Lennard00]. Unequal Progress Across Different Technologies

Importantly, major advances have been limited so far to the digital world. While hardware/software solutions have been elevated to the codesign level [Bolsens97], analog/RF design, despite slow but steady progress, struggles at the synthesis, reuse, and modeling steps [Chang96b, Vassiliou99, Miliozzi00, Gielen00]. As a consequence, the differences in modeling effort, model complexity, and model quality create significant problems at the codesign level. Similarly, considerable state of the art has been established for power-aware digital design techniques [Singh95, Benini99]. At the analog side, low-power design happens too but at a much slower pace and in a much less structured and automated way [Abidi00]. Lack of Techniques to Handle Multiobjective Optimization Complexity

The addition of power consumption as a major additional constraint is particularly serious for the design of wireless, portable consumer devices. The performance-driven state-of-the-art built up in the PC era cannot be reused in the domain of portable wireless devices. General-purpose off-the-shelf microprocessors (0.1 GOPS W−1) have to compete with custom-designed, far more efficient ASICs (100 GOPS W−1) spanning up a design space gap of three orders of magnitude [Claasen99]. Domain-specific architectures with the optimum tradeoff between performance/power and design cost are needed [Schaumont01a].

At the same time, well-known single-objective optimization strategies cannot handle anymore the additional concurrent constraints. Multiobjective optimization strategies have to be introduced and tailored. Moreover, adequate figures-of-merit need to be defined [Papalambros00, Allais43, Pareto06, vanLamsweerde03]. Exploration and optimization require parameterized but not entirely open white-box models. Preferably, gray-box models 33 19 are used that exhibit the relevant parameters to be optimized to the user while hiding irrelevant details [Büchi97, Melgaard94]. Lack of an Executable Specification as Starting Point

The majority of formal design approaches relies on the availability of an executable specification to apply transformation or refinement methods [Lieverse99, Kienhuis99, Catthoor98a, Catthoor98b]. Specifically in the telecommunications domain, this assumption is not met. First, standardization does not provide a complete specification, which requires an exploration of the function design space. Second, multiple concurrent constraints often force a redefinition of the application requirements to obtain an overall feasible design. Both cases require modifications of the executable specification. The problem of functional iterations has been termed algorithm selection [Potkonjak99]. It is essentially the complementary problem to the implementation platform selection. However, it has rarely been addressed in a methodic way. 34 20

2.2.3 2.2.3 Synthesis of a Future-Proof Design Methodology

The previous section indicated a significant gap between the capabilities of the current state of the art in microelectronic system design and the expectations and requirements from both a product design and process technology point of view. In fact, this situation will aggravate with the advent of next-generation multistandard multimedia communication devices. Hence, for our design flow, we neither can rely on selecting existing design methods nor should develop new methods that only fit the design of WLAN transceivers. Instead, we aim at a future-proof design methodology.

First, we stress an integrated view on design space exploration and optimization. Second, we recommend transition-aware modeling and codesign. Finally, we emphasize the importance of a comprehensive design process rationale including nontechnical factors as a measure of success. An Integrated View on Design Space Exploration and Optimization

Design methodology for next-generation communication devices will need to address diverse flexibility requirements [Rappaport02] and diverging interests of multiple players ranging from operators over device manufacturers to users [Pereira01]. Devices need to meet a multitude of discrete standards. In fact, we cannot expect a convergence to one flexible standard, 35 21 but we will need cooperative interaction, compatible services, and standard-compliant devices. This adds the application design space as a third dimension to the existing functional and architectural exploration design space (Fig. 2.5). Efficient exploration techniques are needed for all three subspaces together with a common change management based on multiobjective optimization principles. 36 22
Fig. 2.5.

The extended Y-chart integrates the exploration of the application, function, and architecture design space with a change management component

Efficient design solutions will employ interface- and platform-based architectural concepts together with generic, scalable functional concepts to allow adaptation to changing application scenarios. One part of this design flow will be executed by the designer at design time, while the other part, notably a subset of instance selection, change management, and optimization procedure, will be refined to an implementation which resides in the actual device for intelligent self-adaptation at run time.

The principles of design space exploration, multiobjective optimization, and design-time/run-time tradeoff will be employed throughout Chaps. 4–7. Transition-Aware Modeling and Codesign

We have already stressed earlier that design space exploration and optimization can impossibly be performed at low abstraction levels of design. Hence, we require an adequate vertical design flow, which allows the extraction and embedding of behavioral models, and a fast mapping process to analyze results of a design configuration. Particular attention is given to reduce transition overhead between abstraction levels during specification and simulation as well as the lowering of the entry threshold for new designers. An important role plays the adequate construction and usage of gray-box models (Fig. 2.6).
Fig. 2.6.

Black- and white-box modeling approaches have contradictory properties. Efficient use may require a gray-box model or automated support for model-level transitions

An efficient vertical top-down flow for digital design, its extension to mixed-signal system-level design, and behavioral modeling and gray-box techniques for scalable analog components will be presented in Chap. 7. A Comprehensive Design Process Rationale

In 1993, a number of CAD solutions were introduced as the solution to the ESL design problem. At that time, the approach failed: ESL design became the bad notion of a buzzword and it took years more until recognizable success could be achieved [Goering03]. Our analysis of the design process reveals that the complex interplay between many contravening influences — to a large part nontechnical ones — prohibits a single design methodology or tool from solving the problem. We emphasize the difficulty of managing the design process as a whole and a systematic, horizontal design space exploration as the key enabling techniques of the future. 37 23

Since the early days, methodology and tool support for ESL design have been steadily but slowly improving. Still, most ESL tool solutions of today, e.g., the CoWare tool suite, address only a part of the embedded system design process. The main focus has been on hardware/software codesign at the digital platform level. Improvements are still required, for example, on the integration with analog/RF and on the mapping of functional code toward the platform. Hence, we live up to the rationale defined in Sect. 2.1.2 and evaluate the developed methodology in a broader, also nontechnical context. More details can be found in the conclusions of Chap. 2 and in Chap. 7.

2.3 2.3 Crossdisciplinarity

Design has been described as a structured process, yet tying together a multitude of diverse design and technology disciplines. Obviously, a unidisciplinary approach cannot provide a satisfying solution to the intrinsic diversity. We advocate a crossdisciplinary approach to design, which embeds all layers of disciplinarity. Importantly, we try to bring together the classically divided processes of designing the application and designing the design methodology and advocate for a codesign of application and methodology design. The consequences of this approach for the design process and the designers in a design team are analyzed. We stress that the codesign itself requires a transdisciplinary approach.

2.3.1 2.3.1 Disciplines

The term disciplinarity encapsulates the experience and the final state-of-the-art approaches for the design process and its individual techniques within a particular discipline. In classical terms, microwave/RF, analog, and digital design are considered as three different disciplines. The degree of interaction in a project between team members originating from different disciplines leads to the following classification [Rosenfield92, Bannon94, d'Hainaut86]:
  • Unidisciplinarity means that representatives of individual disciplines meet, but the meeting does not affect their disciplinary identity at all; this often leads to difficulties in understanding since each discipline has its own terminology and standards for information exchange.

  • Multidisciplinarity states that researchers work in parallel or sequentially from a disciplinary-specific base to address a common problem. A basic requirement is that a minimum level of common terminology is established early to allow a partitioning of the common problem into subproblems with their own specifications. The limited terminology forces the use of a top-down-only approach for partitioning, which leads to a suboptimal partitioning with a high probability. In practice, collaboration is often depending on a system architect linking together team members. 38 24 An effective horizontal design space exploration is not possible.

  • Interdisciplinarity requires researchers to work jointly but still from a disciplinary-specific basis to address a common problem. Joint work goes further than an initial partitioning and involves a combination of top-down and bottom-up process steps. This enables design space exploration.

  • Transdisciplinarity assumes a shared conceptual framework, which brings together disciplinary-specific ideas and approaches to address a common problem. Consequently, an early partitioning of the problem into multiple disciplines is not required; instead, all divide and conquer strategies are available. Transdisciplinarity requires a significant multi-disciplinarity per team member to distinguish itself from an interdisciplinary approach.

Crossdisciplinarity is a term that covers multi-, inter-, and transdisciplinarity. We will not use this term since we want to stress their subtle, but important differences between them at different layers in the design process.

2.3.2 2.3.2 Consequences for the Design Process

We can now analyze a design process in terms of its disciplinarity needs (Fig. 2.7). Four levels of design can be distinguished from highest to lowest abstraction level: design space definition, horizontal design space exploration, horizontal partitioning, and vertical design space exploration. Vertical design space exploration is the domain of the classical, unidisciplinary designer. Horizontal partitioning consists of taking quantitative design partitioning decisions and derivation of independent specifications for multiple vertical design space exploration trajectories. The required common terminology and quantitative information exchange ask for a multidisciplinary approach. Horizontal design space exploration goes beyond the partitioning decision because it requires an in-depth understanding of tradeoffs between different disciplines. An example is a clear understanding of the model accuracy vs. computational complexity tradeoff across different disciplines and the capability to specify model requirements. This interactivity, which closes the DSE loop, requires interdisciplinary capabilities. Finally, the definition of the design space requires a system-wide reasoning capability, 39 25 be it qualitative or quantitative. The large complexity at this level requires significant abstraction capabilities and experience for relevance. The commonalities of heterogeneous subsystems must be extracted to make them comparable. This is also the level where ad hoc decisions have to be taken to extend the design space with particular technologies for further exploration.
Fig. 2.7.

Different levels of disciplinarity are required during the design process. Required crossdisciplinarity increases with heterogeneity and abstraction level

In practice, we can often observe a clash between system and circuit designers when it comes to exchanging information about a design because of the different abstraction levels handled by default by both classes of designers. Fairly often, misunderstandings result in either wrong decisions being taken or problems not being recognized early enough. Hence, translation between abstraction levels and reasoning at various abstraction levels is an important skill for both system and circuit designers. Note that, for the circuit designer, this does not require an extension of his disciplinary scope to other disciplines.

Fig. 2.7 clearly indicates that different levels of disciplinarity are required. It also shows that systems with limited heterogeneity may not exploit capabilities at all levels.

2.3.3 2.3.3 Consequences for the Designers and Design Methodologies

Changes in the design process are reflected both in requirements for the application designers and the design methodologies applied by them. Increased Need for Interdisciplinarity in Application Design

When we consider the particular case of wireless system design, the designers face both a complex, heterogeneous design case and a large number of conflicting design objectives. This requires a detailed horizontal design space exploration, such that a close-to-optimal solution is found. Hence, we can state an increased need for interdisciplinary methodologies and designers. This conclusion seems to be in contrast with several statements in [Bannon94] where a multidisciplinary approach for the discipline of system development is thought to be sufficient. In fact, there is no contradiction but this evolution toward interdisciplinarity reflects the steadily increasing performance/cost pressure in the wireless and consumer market [Wheeler03]. Link Between Role Models and Disciplinarity for Application Designers

Muller [Muller03] describes the stepwise development from a unidisciplinary trained designer toward a system architect. This reflects the transition from a specialist in a narrow discipline toward a generalist with a broad experience. Importantly, each generalist should have developed a feeling for the design complexity depth, i.e., for the vertical DSE process (unidisciplinary root know-how). Fig. 2.8 applies this principle to integrated system design. Designing a system requires — in one way or another — filling the box of competences which is span across several disciplines and across several levels of abstraction. Lacking competences in the picture lead either to local, suboptimal contributions or even to risks since implications of particular technologies remain unknown. Disciplinarity increases with the breadth of know-how. Obviously, human capabilities in general do not allow the combination of deep know-how in a large number of disciplines. Hence, a specialization in vertical or horizontal know-how results. This reflects very well the classification of disciplinarity mentioned above.
Fig. 2.8.

Since depth and breadth of know-how are difficult to combine, system design requires a balance of all disciplinary capabilities supporting each other

Opportunities to replace designers' experience by external IP appear mainly at the unidisciplinary level. In SoC or SiP design, this was first limited to hard or soft macros or modules. The extension toward programmable platforms, e.g., embedded processors and bus systems, FPGAs, etc., reflects already a reduction of design complexity at the multidisciplinary level since these components hide partitioning decisions [Chang99]. Increased Need for Transdisciplinarity in Design Technology Development

The development of design technology has always been an interdisciplinary task. It requires knowledge in a particular application domain and, for the implementation of the design technology, in software engineering and algorithms, and digital signal processing. The move to system-level design brings multiple domains together along with their individual tool implementation knowledge. Unless embedded in a single conceptual framework, 40 26 application of methodologies and tools at the system level will become inefficient and unattractive to the designer. A transdisciplinary approach can prevent the creation of a pile of incompatible tools and, instead, provide suggestions for domain-specific design flows and hence guide the development of the essential tools and support for the relevant design step transitions. Importantly, only the combination of application-or domain-specific know-how with insight into the capabilities of design technology can produce a design process as proposed in Sect. 2.1, which truly enables a design team.

2.3.4 2.3.4 Codesign of Design Technology and Application

The heterogeneity of integrated electronic systems does not allow finding a perfect match of design tools when the design starts without limiting design creativity too much and too early in a deliberate way. While a basic set of tools, mainly at the unidisciplinary level, can be reused due to its generality, higher abstraction layers will require more dedicated approaches to efficiently define, explore, and optimize the system. Also, transitions between selected tools may not fit perfectly the desired design flow.

Traditionally, this resulted in a black-or-white decision [Schaumont01a]: The “pure design technologist” would wait for the design of the optimum tools before starting a design while the “designer-in-the-trenches” would abandon any new tool development and go for the working chip first. Both approaches are undesirable since they either result in tools that come too late for use in practical designs, or vice versa, no progress in tools ever and a nonscalable and hence unpredictable way of designing. Actually, the strong connection between application domain and suited design methodologies allows industry to turnaround this vicious circle. In-house codevelopment of application know-how and domain-specific design methodologies can lead to a competitive advantage over competitors in terms of design efficiency and hence design cost and quality. Similar to the customer-owned tooling (COT) approach at the physical design level, we may call this concurrent process a customer-owned system design process.

Consequently, we motivate a joint path for application and design technology in an integrated codesign process. The idea for the codesign of application design and design methodology is not new and has been stressed, for example, in “you have to design the [design] environment plus the chips together” [DeMan00] or “design the design system for your system” [Schaumont01a]. This work illustrates this codesign idea in an explicit transdisciplinary way, which is based on three principles. First, we start with an application goal and not with a particular design problem. Second, we develop design methodology only when it proves to be necessary to overcome a specific application design problem in the foreseen path. Third, each developed design methodology needs to be evaluated with the problem that triggered its development and at least one other example that allows conclusions about the generalization of the method.

We illustrate the codesign of design technology along with the application design for three design challenges (Chap. 7):
  • A smooth, efficient design flow for digital VLSI design from the system to the gate level; an analysis of reuse cases demonstrates that an initial investment in design methodology pays off

  • An extension of this digital flow with an analog/RF component that allows mixed-signal system simulation and hence horizontal design space exploration

  • A methodology for design space exploration at the communications link level, at the mixed-signal system level, and at the component level, which allows a partitioning of the system complexity into a design-time and a run-time aspect

2.4 2.4 Conclusions

This chapter puts the focus on the design task itself, its implications for the designer and the design team but also for the outcome and output of the design project. Today's communications systems have become very complex and depend on close collaboration of many disciplines. The resulting design process is full of interactions and requires careful planning, observation, and steering. An important evolution complicating this process is that, beyond the continuous pressure for short design time and low design cost, performance of wireless devices must increase while energy consumption must decrease. The latter dilemma requires optimization across disciplinary boundaries to obtain the aggressive design targets. A consequence of this evolution is the move toward crossdisciplinary exploration and optimization which call for novel methodologies and tool support to ease the designers' work.

The concrete design examples in the following chapters will illustrate how we translated this methodology into practice. Chapters 4–6 are not object oriented but goal oriented and the accompanying process is driven by elimination of the major design obstacles in terms of performance and/or power. In Chap. 6, this culminates in a methodology that systematically trades off performance and energy cost in a multiobjective optimization approach. Importantly, this approach can be used early in the design process and updated later, leading to an encapsulation of requirements specification, methodology, and actual design into a single process.

In particular, Chap. 7 addresses how particular design challenges were translated into methodologies and supported by tools. There, we show how a concurrent development of design methods for digital design, mixed analog/digital codesign and modeling, and system-wide optimization results not only in an ad hoc design result but also in a generic reusable methodology that is applicable to a wider range of problems.


  1. 1.

    15 Schopenhauer's words — “Jeder Mensch hält die Grenzen der Wahrnehmung für die Grenzen der Welt.” — obviously go beyond simple perceptions of working style; but has not there always been a fight between old and new methods of accomplishing things? Schopenhauer mentions also that this often includes that new thoughts are first ridiculed, then attacked, and finally become common thinking — “Ein neuer Gedanke wird zuerst verlacht, dann bekämpft, bis er nach längerer Zeit als selbstverständlich gilt.”

  2. 2.

    16 Obviously, products are designed everyday. A fundamental question is how much experience from previous designs plays a role to reproduce a design success and whether and how some of this experience can be transferred from an experienced human designer to other designers, into a methodology, or into tools.

  3. 3.

    17 The word design has its origin in the Latin word designare which means to devise (to invent), to mark out for something.

  4. 4.

    18 A system can be defined as a set of interrelated elements perceived as a whole, performing a useful function by interaction with human or other environment. A system is composed of components, typically in a hierarchical way. The interrelation makes the system more useful than the sum of its isolated components. The system reacts to or interacts with its environment.

  5. 5.

    19 “This made me think that there is no bottom, no top, and every year more steps are added as our field gets chopped into more manageable segments. I look down the stairs [of the design process] and see them disappearing into blackness. Above me the steps ascend into fog.” [Lucky03a]

  6. 6.

    20 These are quotes originating from various microelectronics company websites.

  7. 7.

    21 KAOS, keep all objectives satisfied.

  8. 8.

    22 We can ask the question whether we need a fully formalized specification process; regarding the what-aspect, this appears useful. However, most misunderstandings are correlated with mistakes on the why-aspects, i.e., with the derivation of wrong specifications. Formalizing the why-process appears as an even more challenging task.

  9. 9.

    23 A method is a means or manner or procedure to accomplish something in a regular and systematic way [AHD00]. A methodology establishes a body of methods and principles particular to a particular domain. Interestingly, the authors also state the misuse of the term methodology as a pretentious substitute for method.

  10. 10.

    24 Reference [AHD00] states that misuse of the term methodology as a pretentious substitute in case of nongeneralizable methods has unfortunately become a widespread phenomenon.

  11. 11.

    25 Technology is the systematic treatment of an art, craft, or technique [AHD00].

  12. 12.

    26 The Greek meta means “beyond, after.” Interesting from a linguistic point of view, method stems from the Greek methodos, which is already an agglomeration of meta and hodos (the latter meaning way, journey). Thus, the word method alone addresses already the metacharacter.

  13. 13.

    27 I. Kant used the slightly different terms of cognitive powers, feeling of pleasure and displeasure, and power of desire in 1790. See also [Stoltermann92].

  14. 14.

    28 Several quotes from Cadence Design Systems, Simplex, and at the International Symposium on Quality Electronic Design, March 27, 2001; based on a study of Collet International, 2000.

  15. 15.

    29 Estimate as of Cadence Design Systems, 2003.

  16. 16.

    30 M. Santarini, “Panel debates viability of ESL tools market,” EEdesign, March 5, 2004.

  17. 17.

    31 The concept of transactions actually covers a large range of abstractions and is hence not very well described. An example is the modeling of an actually time-distributed physical bus access as a single transaction with a lumped timing and energy consumption. However, we could also go further and represent several related bus accesses (a burst access) again as a single transaction. The goal of transaction level modeling is finally representing the detailed interaction by its relevant properties.

  18. 18.

    32 Intellectual property.

  19. 19.

    33 Gray-box modeling originates from system identification, e.g., Bohlin, 1984.

  20. 20.

    34 In fact, Potkonjak and Rabaey [Potkonjak99] state: “this area is currently more art than science or engineering and designers almost exclusively rely on intuition instead of accurate quantitative procedures. Very likely that this goal will remain elusive for years to come.”

  21. 21.

    35 Citing Goodman in [Wickelgren96]: “Flexible standards is an oxymoron. If it's too flexible, it's not a standard.”

  22. 22.

    36 Note that our view goes beyond the definition of system-level design as a third discipline between algorithmic exploration and implementation [Meyr01].

  23. 23.

    37 Traditionally, design space exploration and design process have rather been treated as a cost factor, although both enable a larger design space and hence are key to a better, if not to the optimal solution.

  24. 24.

    38 Unfortunately, system architects with a bird's eye view on a system-wide scale are scarce.

  25. 25.

    39 System-wide reasoning capability is hard to find since it requires a combination of broad experience and fast mental abstraction skills. The experience part may require some time in practice. Mental abstraction skills, however, can be taught and trained to some extent.

  26. 26.

    40 Note that we do not mean here what CAD vendors call an “integrated framework,” i.e., a tool suite with a common GUI, etc.


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