Header

UZH-Logo

Maintenance Infos

Brain-inspired methods for achieving robust computation in heterogeneous mixed-signal neuromorphic processing systems


Zendrikov, Dmitrii; Solinas, Sergio; Indiveri, Giacomo (2023). Brain-inspired methods for achieving robust computation in heterogeneous mixed-signal neuromorphic processing systems. Neuromorphic Computing and Engineering, 3(3):034002.

Abstract

Neuromorphic processing systems implementing spiking neural networks with mixed signal analog/digital electronic circuits and/or memristive devices represent a promising technology for edge computing applications that require low power, low latency, and that cannot connect to the cloud for off-line processing, either due to lack of connectivity or for privacy concerns. However, these circuits are typically noisy and imprecise, because they are affected by device-to-device variability, and operate with extremely small currents. So achieving reliable computation and high accuracy following this approach is still an open challenge that has hampered progress on the one hand and limited widespread adoption of this technology on the other. By construction, these hardware processing systems have many constraints that are biologically plausible, such as heterogeneity and non-negativity of parameters. More and more evidence is showing that applying such constraints to artificial neural networks, including those used in artificial intelligence, promotes robustness in learning and improves their reliability. Here we delve even more into neuroscience and present network-level brain-inspired strategies that further improve reliability and robustness in these neuromorphic systems: we quantify, with chip measurements, to what extent population averaging is effective in reducing variability in neural responses, we demonstrate experimentally how the neural coding strategies of cortical models allow silicon neurons to produce reliable signal representations, and show how to robustly implement essential computational primitives, such as selective amplification, signal restoration, working memory, and relational networks, exploiting such strategies. We argue that these strategies can be instrumental for guiding the design of robust and reliable ultra-low power electronic neural processing systems implemented using noisy and imprecise computing substrates such as subthreshold neuromorphic circuits and emerging memory technologies.

Abstract

Neuromorphic processing systems implementing spiking neural networks with mixed signal analog/digital electronic circuits and/or memristive devices represent a promising technology for edge computing applications that require low power, low latency, and that cannot connect to the cloud for off-line processing, either due to lack of connectivity or for privacy concerns. However, these circuits are typically noisy and imprecise, because they are affected by device-to-device variability, and operate with extremely small currents. So achieving reliable computation and high accuracy following this approach is still an open challenge that has hampered progress on the one hand and limited widespread adoption of this technology on the other. By construction, these hardware processing systems have many constraints that are biologically plausible, such as heterogeneity and non-negativity of parameters. More and more evidence is showing that applying such constraints to artificial neural networks, including those used in artificial intelligence, promotes robustness in learning and improves their reliability. Here we delve even more into neuroscience and present network-level brain-inspired strategies that further improve reliability and robustness in these neuromorphic systems: we quantify, with chip measurements, to what extent population averaging is effective in reducing variability in neural responses, we demonstrate experimentally how the neural coding strategies of cortical models allow silicon neurons to produce reliable signal representations, and show how to robustly implement essential computational primitives, such as selective amplification, signal restoration, working memory, and relational networks, exploiting such strategies. We argue that these strategies can be instrumental for guiding the design of robust and reliable ultra-low power electronic neural processing systems implemented using noisy and imprecise computing substrates such as subthreshold neuromorphic circuits and emerging memory technologies.

Statistics

Citations

Dimensions.ai Metrics
4 citations in Web of Science®
3 citations in Scopus®
Google Scholar™

Altmetrics

Downloads

2 downloads since deposited on 30 Jan 2024
2 downloads since 12 months
Detailed statistics

Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Neuroinformatics
04 Faculty of Medicine > University Children's Hospital Zurich > Medical Clinic
04 Faculty of Medicine > University Hospital Zurich > Clinic for Neurosurgery
Dewey Decimal Classification:610 Medicine & health
570 Life sciences; biology
Scopus Subject Areas:Physical Sciences > Artificial Intelligence
Physical Sciences > Hardware and Architecture
Physical Sciences > Electrical and Electronic Engineering
Physical Sciences > Electronic, Optical and Magnetic Materials
Uncontrolled Keywords:Education, Cultural Studies
Language:English
Date:1 September 2023
Deposited On:30 Jan 2024 14:46
Last Modified:30 Apr 2024 01:48
Publisher:IOP Publishing
ISSN:2634-4386
OA Status:Gold
Free access at:Publisher DOI. An embargo period may apply.
Publisher DOI:https://doi.org/10.1088/2634-4386/ace64c
Project Information:
  • : FunderH2020
  • : Grant ID724295
  • : Project TitleNeuroAgents - Neuromorphic Electronic Agents: from sensory processing to autonomous cognitive behavior
  • : FunderBando Fondazione di Sardegna
  • : Grant ID
  • : Project Title
  • Content: Published Version
  • Language: English
  • Licence: Creative Commons: Attribution 4.0 International (CC BY 4.0)