NVMLearn: A simulation platform for non-volatile-memory-based deep learning hardware

Darsen D. Lu, Fu Xiang Liang, Yi Ci Wang, Huai Kuan Zeng

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

2 Citations (Scopus)

Abstract

Hardware implementation of deep machine learning using the convolutional neural network has been successfully demonstrated using array architecture with non-volatile storage elements such as floating-gate MOS transistor, resistive memory, phase change memory, etc. We present a new simulation platform, NVMLearn, to aid the design, verification, and system-level power and performance estimation for such architecture. Physical characteristics of memory devices are modeled using Verilog-A compact models, which can be easily simulated in SPICE to obtain the device programming, erasure, and read behavior. On the system level, NVMLearn simulates the training of the entire convolutional network based on any non-volatile memory device type.

Original languageEnglish
Title of host publicationProceedings of the 2017 IEEE International Conference on Applied System Innovation
Subtitle of host publicationApplied System Innovation for Modern Technology, ICASI 2017
EditorsTeen-Hang Meen, Artde Donald Kin-Tak Lam, Stephen D. Prior
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages66-69
Number of pages4
ISBN (Electronic)9781509048977
DOIs
Publication statusPublished - 2017 Jul 21
Event2017 IEEE International Conference on Applied System Innovation, ICASI 2017 - Sapporo, Japan
Duration: 2017 May 132017 May 17

Publication series

NameProceedings of the 2017 IEEE International Conference on Applied System Innovation: Applied System Innovation for Modern Technology, ICASI 2017

Other

Other2017 IEEE International Conference on Applied System Innovation, ICASI 2017
CountryJapan
CitySapporo
Period17-05-1317-05-17

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Computer Science Applications
  • Hardware and Architecture
  • Safety, Risk, Reliability and Quality
  • Mechanical Engineering
  • Media Technology
  • Health Informatics
  • Instrumentation

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