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Making neural encoding robust and energy efficient: An advanced analog temporal encoder for brain-inspired computing systems

Authors:

Yang YiAuthors Info & Claims

2016 IEEE/ACM International Conference on Computer-Aided Design (ICCAD)

Pages 1 - 6

https://doi.org/10.1145/2966986.2967052

Published: 07 November 2016 Publication History

Abstract

Neural encoder is one of the key components in neuromorphic computing systems, whereby sensory information is transformed into spike coded trains. The design of temporal encoder has attracted a widespread attention in the field of neuromorphic computing in the past few years. The information in the temporal encoding scheme with inter-spike intervals can arise from correlations between spike times, which could not be incorporated in the traditional rate encoding scheme. In this paper, we propose a robust and energy efficient analog implementation of the spiking temporal encoder. We pattern the neural activities across multiple timescales and encode the sensory information using time dependent temporal scales. The concept of iteration structure is introduced to construct a neural encoder that greatly increases the information process ability of the proposed temporal encoder. Integrated with iteration technique and operational-amplifier-free design, the error rate of the output temporal codes is reduced to an extremely low level. A lower sampling rate accompanied by additional verification spikes is introduced in the schemes, which significantly reduces the power consumption of the encoding system. To the best of our knowledge, our proposed neuron circuit is the first analog hardware implementation of the neural encoder that could present the sensory data using inter-spike interval temporal encoding scheme. The simulation and measurement results show the proposed temporal encoder exhibits not only energy efficiency but also high accuracy.

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Cited By

Zheng H(Cindy) Yi Y(2023)Spiking Neural Encoding and Hardware Implementations for Neuromorphic ComputingNeuromorphic Computing10.5772/intechopen.113050Online publication date: 15-Nov-2023
https://doi.org/10.5772/intechopen.113050
Zheng HBai KYi Y(2023)Enabling a New Methodology of Neural Coding: Multiplexing Temporal Encoding in Neuromorphic ComputingIEEE Transactions on Very Large Scale Integration (VLSI) Systems10.1109/TVLSI.2023.323451431:3(331-342)Online publication date: Mar-2023
https://doi.org/10.1109/TVLSI.2023.3234514
Date PKulkarni SYoung ASchuman CPotok TVetter J(2023)Encoding integers and rationals on neuromorphic computers using virtual neuronScientific Reports10.1038/s41598-023-35005-x13:1Online publication date: 6-Jul-2023
https://doi.org/10.1038/s41598-023-35005-x
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Index Terms

Making neural encoding robust and energy efficient: An advanced analog temporal encoder for brain-inspired computing systems
1. Computer systems organization
2. Hardware
  1. Emerging technologies
  2. Very large scale integration design

Index terms have been assigned to the content through auto-classification.

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2016 IEEE/ACM International Conference on Computer-Aided Design (ICCAD)

Nov 2016

946 pages

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IEEE Press

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Published: 07 November 2016

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Cited By

Zheng H(Cindy) Yi Y(2023)Spiking Neural Encoding and Hardware Implementations for Neuromorphic ComputingNeuromorphic Computing10.5772/intechopen.113050Online publication date: 15-Nov-2023
https://doi.org/10.5772/intechopen.113050
Zheng HBai KYi Y(2023)Enabling a New Methodology of Neural Coding: Multiplexing Temporal Encoding in Neuromorphic ComputingIEEE Transactions on Very Large Scale Integration (VLSI) Systems10.1109/TVLSI.2023.323451431:3(331-342)Online publication date: Mar-2023
https://doi.org/10.1109/TVLSI.2023.3234514
Date PKulkarni SYoung ASchuman CPotok TVetter J(2023)Encoding integers and rationals on neuromorphic computers using virtual neuronScientific Reports10.1038/s41598-023-35005-x13:1Online publication date: 6-Jul-2023
https://doi.org/10.1038/s41598-023-35005-x
Pan HSong HZhang QMi W(2022)Review of Closed-Loop Brain–Machine Interface Systems From a Control PerspectiveIEEE Transactions on Human-Machine Systems10.1109/THMS.2021.313867752:5(877-893)Online publication date: Oct-2022
https://doi.org/10.1109/THMS.2021.3138677
Date PKulkarni SYoung ASchuman CPotok TVetter J(2022)Virtual Neuron: A Neuromorphic Approach for Encoding Numbers2022 IEEE International Conference on Rebooting Computing (ICRC)10.1109/ICRC57508.2022.00017(100-105)Online publication date: Dec-2022
https://doi.org/10.1109/ICRC57508.2022.00017
Zheng HMohammadi NBai KYi Y(2021)Low-power Analog and Mixed-signal IC Design of Multiplexing Neural Encoder in Neuromorphic Computing2021 22nd International Symposium on Quality Electronic Design (ISQED)10.1109/ISQED51717.2021.9424267(154-159)Online publication date: 7-Apr-2021
https://doi.org/10.1109/ISQED51717.2021.9424267
Liu SYi Y(2020)Quantized Neural Networks and Neuromorphic Computing for Embedded SystemsIntelligent System and Computing [Working Title]10.5772/intechopen.91835Online publication date: 30-Mar-2020
https://doi.org/10.5772/intechopen.91835
Fang HMei ZShrestha AZhao ZLi YQiu QXie Y(2020)Encoding, model, and architectureProceedings of the 39th International Conference on Computer-Aided Design10.1145/3400302.3415608(1-9)Online publication date: 2-Nov-2020
https://dl.acm.org/doi/10.1145/3400302.3415608
Bai KYi Y(2019)Opening the “Black Box” of Silicon Chip Design in Neuromorphic ComputingBio-Inspired Technology [Working Title]10.5772/intechopen.83832Online publication date: 8-Mar-2019
https://doi.org/10.5772/intechopen.83832
Zhao CLiu LYi YPotok TSchuman C(2019)Design and Analysis of Real Time Spiking Neural Network Decoder for Neuromorphic ChipsProceedings of the International Conference on Neuromorphic Systems10.1145/3354265.3354280(1-4)Online publication date: 23-Jul-2019
https://dl.acm.org/doi/10.1145/3354265.3354280
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