NeuroSim+: An integrated device-to-algorithm framework for benchmarking synaptic devices and array architectures

Pai Yu Chen, Xiaochen Peng, Shimeng Yu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

28 Scopus citations

Abstract

Neuro Sim+ is an integrated simulation framework for benchmarking synaptic devices and array architectures in terms of the system-level learning accuracy and hardware performance metrics. It has a hierarchical organization from the device level (transistor technology and memory cell models) to the circuit level (synaptic array architectures and neuron periphery) and then to the algorithm level (neural network topologies). In this work, we study the impact of the "analog" eNVM non-ideal device properties and benchmark the trade-offs of SRAM, digital and analog eNVM based array architectures for online learning and offline classification. The source code of NeuroSim+ version 1.0 is publicly available at https://github.com/neurosim/MLP-NeuroSim.

Original languageEnglish (US)
Title of host publication2017 IEEE International Electron Devices Meeting, IEDM 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages6.1.1-6.1.4
ISBN (Electronic)9781538635599
DOIs
StatePublished - Jan 23 2018
Event63rd IEEE International Electron Devices Meeting, IEDM 2017 - San Francisco, United States
Duration: Dec 2 2017Dec 6 2017

Other

Other63rd IEEE International Electron Devices Meeting, IEDM 2017
CountryUnited States
CitySan Francisco
Period12/2/1712/6/17

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ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Electrical and Electronic Engineering
  • Materials Chemistry

Cite this

Chen, P. Y., Peng, X., & Yu, S. (2018). NeuroSim+: An integrated device-to-algorithm framework for benchmarking synaptic devices and array architectures. In 2017 IEEE International Electron Devices Meeting, IEDM 2017 (pp. 6.1.1-6.1.4). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IEDM.2017.8268337