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Ultra-Low-Power FDSOI Neural Circuits for Extreme-Edge Neuromorphic Intelligence


Rubino, Arianna; Livanelioglu, Can; Qiao, Ning; Payvand, Melika; Indiveri, Giacomo (2020). Ultra-Low-Power FDSOI Neural Circuits for Extreme-Edge Neuromorphic Intelligence. IEEE Transactions on Circuits and Systems. Part 1: Regular Papers, 68(1):45-56.

Abstract

Recent years have seen an increasing interest in the development of artificial intelligence circuits and systems for edge computing applications. In-memory computing mixed-signal neuromorphic architectures provide promising ultra-low-power solutions for edge-computing sensory-processing applications, thanks to their ability to emulate spiking neural networks in real-time. The fine-grain parallelism offered by this approach allows such neural circuits to process the sensory data efficiently by adapting their dynamics to the ones of the sensed signals, without having to resort to the time-multiplexed computing paradigm of von Neumann architectures. To reduce power consumption even further, we present a set of mixed-signal analog/digital circuits that exploit the features of advanced Fully-Depleted Silicon on Insulator (FDSOI) integration processes. Specifically, we explore the options of advanced FDSOI technologies to address analog design issues and optimize the design of the synapse integrator and of the adaptive neuron circuits accordingly. We present circuit post-layout simulation results and demonstrate the circuit’s ability to produce biologically plausible neural dynamics with compact designs, optimized for the realization of large-scale spiking neural networks in neuromorphic processors.

Abstract

Recent years have seen an increasing interest in the development of artificial intelligence circuits and systems for edge computing applications. In-memory computing mixed-signal neuromorphic architectures provide promising ultra-low-power solutions for edge-computing sensory-processing applications, thanks to their ability to emulate spiking neural networks in real-time. The fine-grain parallelism offered by this approach allows such neural circuits to process the sensory data efficiently by adapting their dynamics to the ones of the sensed signals, without having to resort to the time-multiplexed computing paradigm of von Neumann architectures. To reduce power consumption even further, we present a set of mixed-signal analog/digital circuits that exploit the features of advanced Fully-Depleted Silicon on Insulator (FDSOI) integration processes. Specifically, we explore the options of advanced FDSOI technologies to address analog design issues and optimize the design of the synapse integrator and of the adaptive neuron circuits accordingly. We present circuit post-layout simulation results and demonstrate the circuit’s ability to produce biologically plausible neural dynamics with compact designs, optimized for the realization of large-scale spiking neural networks in neuromorphic processors.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Neuroinformatics
Dewey Decimal Classification:570 Life sciences; biology
Scopus Subject Areas:Physical Sciences > Electrical and Electronic Engineering
Uncontrolled Keywords:Electrical and Electronic Engineering
Language:English
Date:16 November 2020
Deposited On:15 Feb 2021 10:14
Last Modified:16 Feb 2021 21:01
Publisher:Institute of Electrical and Electronics Engineers
ISSN:1549-8328
OA Status:Closed
Publisher DOI:https://doi.org/10.1109/tcsi.2020.3035575
Project Information:
  • : FunderH2020
  • : Grant ID724295
  • : Project TitleNeuroAgents - Neuromorphic Electronic Agents: from sensory processing to autonomous cognitive behavior
  • : FunderH2020
  • : Grant ID826655
  • : Project TitleTEMPO - Technology and hardware for neuromorphic computing
  • : FunderH2020
  • : Grant ID871737
  • : Project TitleBeFerroSynaptic - BEOL technology platform based on ferroelectric synaptic devices for advanced neuromorphic processors
  • : FunderH2020
  • : Grant ID871371
  • : Project TitleMeM-Scales - Memory technologies with multi-scale time constants for neuromorphic architectures

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