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NANO-CHIPS 2030 pp 127–151Cite as

Heterogeneous 3D Nano-systems: The N3XT Approach?

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Abstract

Coming generations of information technology will process unprecedented amounts of loosely-structured data, including streaming video and audio, natural languages, real-time sensor readings, contextual environments, or even brain signals. The computational demands of these abundant-data applications, such as deep learning-based AI, far exceed the capabilities of today’s computing systems and cannot be met by isolated improvements in transistor or memory technologies, or integrated circuit architectures alone. This chapter will discuss how new computing nanosystems can deliver radical improvements in the energy efficiency and scalability of abundant-data applications, in the range of 1000×, through major advances across the computing stack: new transistor and memory technologies, new integration approaches, and new architectures for computation immersed in memory. Nanosystems with such massive benefits are essential for enabling new frontiers of applications across a wide range of domains, from highly energy-constrained and deeply-embedded computing systems all the way to the cloud.

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  1. EE, Stanford University, Gates Building, Room 334, 353 Serra Mall, Stanford, CA, 94305, USA

    Dennis Rich, Carlo Gilardo, Haitong Li, Rebecca Park, Robert M. Radway & H.-S. Philip Wong

  2. CS, Stanford University, Gates Building, Room 334, 353 Serra Mall, Stanford, CA, 94305, USA

    Andrew Bartolo & Subhasish Mitra

  3. EE, San Jose State University, Charles W. Davidson College of Engineering Building, Room 365, 1 Washington Square, San Jose, CA, 95192, USA

    Binh Le

  4. SCSE, Nanyang Technological University, 50 Nanyang Avenue, N4-02c-92, Singapore, 639798, Singapore

    Mohamed M. Sabry Aly

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Rich, D. et al. (2020). Heterogeneous 3D Nano-systems: The N3XT Approach?. In: Murmann, B., Hoefflinger, B. (eds) NANO-CHIPS 2030. The Frontiers Collection. Springer, Cham. https://doi.org/10.1007/978-3-030-18338-7_9

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