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.
Original languageEnglish
Title of host publication2019 IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS)
PublisherIEEE
Pages244-245
Number of pages2
ISBN (Electronic)978-1-5386-7884-8
ISBN (Print)978-1-5386-7885-5
DOIs
Publication statusPublished - 2019
Event1st IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2019 - Hsinchu, Taiwan, Province of China
Duration: 18 Mar 201920 Mar 2019

Conference

Conference1st IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2019
CountryTaiwan, Province of China
CityHsinchu
Period18/03/1920/03/19

    Research areas

  • activation functions, deep neural network, extraction, mismatch, spiking neural network, training

ID: 57216547