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Brain-Inspired Architectures for Nanoelectronics

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CHIPS 2020 VOL. 2

Part of the book series: The Frontiers Collection ((FRONTCOLL))

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Abstract

Mapping brain-like structures and processes into electronic substrates has recently seen a revival with the availability of deep-submicron CMOS technology. The basic idea is to exploit the massive parallelism of such circuits and to create low-power and fault-tolerant information-processing systems. Aiming at overcoming the big challenges of deep-submicron CMOS technology (power wall, reliability, design complexity), bio-inspiration offers alternative ways to (embedded) artificial intelligence. The challenge is to understand, design, build, and use new architectures for nanoelectronic systems, which unify the best of brain-inspired information processing concepts and of nanotechnology hardware, including both algorithms and architectures. Obviously, the brain could serve as an inspiration at several different levels, when investigating architectures spanning from innovative system-on-chip to biologically neural inspired. This chapter introduces basic properties of biological brains and general approaches to realize them in nanoelectronics. Modern implementations are able to reach the complexity-scale of large functional units of biological brains, and they feature the ability to learn by plasticity mechanisms found in neuroscience. Combined with high-performance programmable logic and elaborate software tools, such systems are currently evolving into user-configurable non-von-Neumann computing systems, which can be used to implement and test novel computational paradigms. Hence, big brain research programs started world-wide. Four projects from the largest programs on brain-like electronic systems in Europe (Human Brain Project) and in the US (SyNAPSE) will be outlined in this chapter.

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Authors and Affiliations

  1. CITEC, Bielefeld University, Inspiration 1, 33619, Bielefeld, Germany

    Ulrich Rueckert

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  1. Ulrich Rueckert
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Correspondence to Ulrich Rueckert .

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  1. Institute for Microelectronics, Sindelfingen, Germany

    Bernd Höfflinger

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Rueckert, U. (2016). Brain-Inspired Architectures for Nanoelectronics. In: Höfflinger, B. (eds) CHIPS 2020 VOL. 2. The Frontiers Collection. Springer, Cham. https://doi.org/10.1007/978-3-319-22093-2_18

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