Abstract
In modern computing many applications exist which do not require exact results or for which a single golden, correct solution does not exist. Consider web search as an example. The user does not know in what order which results would appear and considers any outcome as acceptable or correct for which the returned results are related to the search topic. These kinds of applications are inherently error tolerant. In particular, applications on mobile devices belong to this class, since they include games, digital media processing, web browsing, etc. However, in all of these applications, some data may be more resilient to errors than others. Approximate Computing (AC) is a design paradigm that tries to make use of this error tolerance by trading accuracy for performance.
In this chapter, we give an overview of techniques used to increase the performance of memory in modern circuits, by reducing the exactness requirements of the stored data (i.e., stored data may be lost or approximated by other stored data). After a short overview of the architecture of both SRAM and DRAM, we review techniques used for data partitioning. This is necessary in order to characterize which data may be stored approximately and which may not. Additionally, we describe techniques to reduce the power consumption as well as the performance of SRAM and DRAM. Finally, we present an approach that relaxes the requirement of exact matching when looking for a previously computed result to speed up the computation.
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- 1.
VDD is the common abbreviation for the supply voltage and GND for ground, respectively.
- 2.
This means that they keep their state until another value is stored.
- 3.
ProACt is based on RISC-V [22], a state-of-the-art open-source 64-bit RISC architecture.
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Acknowledgements
The authors would like to thank Arun Chandrasekharan for his significant contributions to ProACt.
This work was supported in part by the German Research Foundation (DFG) within the project MANIAC (DR 287/29-1), by the Reinhart Koselleck Project DR 287/23-1, and by the University of Bremen’s graduate school SyDe, funded by the German Excellence Initiative.
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Froehlich, S., Große, D., Drechsler, R. (2020). Approximate Memory: Data Storage in the Context of Approximate Computing. In: Große, C., Drechsler, R. (eds) Information Storage. Springer, Cham. https://doi.org/10.1007/978-3-030-19262-4_4
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