Heterogeneous Activation Function Extraction for Training and Optimization of SNN Systems

Conference paper (2019)

Authors

Amir Zjajo Signal Processing Systems -

S.S. Kumar Signal Processing Systems -

T.G.R.M. van Leuken Signal Processing Systems -

Research Group

Signal Processing Systems () (TU Delft)

Training Mismatch Extraction Deep neural network Spiking neural network Activation functions

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http://resolver.tudelft.nl/uuid:7bf40973-7fe1-4109-a414-9917c2861814

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Published Date

2019

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Microelectronics

Research Group

Signal Processing Systems

Abstract

Energy-efficiency and computation capability characteristics of analog/mixed-signal spiking neural networks offer capable platform for implementation of cognitive tasks on resource-limited embedded platforms. However, inherent mismatch in analog devices severely influence accuracy and reliability of the computing system. In this paper, we devise efficient algorithm for extracting of heterogeneous activation functions of analog hardware neurons as a set of constraints in an off-line training and optimization process, and examine how compensation of the mismatch effects influence synchronicity and information processing capabilities of the system.

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