PIMBALL: Binary neural networks in spintronic memory

Salonik Resch, S. Karen Khatamifard, Zamshed Iqbal Chowdhury, Masoud Zabihi, Zhengyang Zhao, Jian Ping Wang, Sachin S. Sapatnekar, Ulya R. Karpuzcu

Research output: Contribution to journalArticle

Abstract

Neural networks span a wide range of applications of industrial and commercial significance. Binary neural networks (BNN) are particularly effective in trading accuracy for performance, energy efficiency, or hardware/ software complexity. Here, we introduce a spintronic, re-configurable in-memory BNN accelerator, PIMBALL: Processing In Memory BNN AcceL(L)erator, which allows for massively parallel and energy efficient computation. PIMBALL is capable of being used as a standard spintronic memory (STT-MRAM) array and a computational substrate simultaneously. We evaluate PIMBALL using multiple image classifiers and a genomics kernel. Our simulation results show that PIMBALL is more energy efficient than alternative CPU-, GPU-, and FPGA-based implementations while delivering higher throughput.

Original languageEnglish (US)
Article numberA41
JournalACM Transactions on Architecture and Code Optimization
Volume16
Issue number4
DOIs
StatePublished - Oct 2019

Fingerprint

Magnetoelectronics
Neural networks
Data storage equipment
Particle accelerators
Program processors
Energy efficiency
Field programmable gate arrays (FPGA)
Classifiers
Throughput
Hardware
Substrates
Processing

Keywords

  • Binary neural networks
  • Computational random access memory
  • Non-volatile memory
  • Processing in memory

Cite this

PIMBALL : Binary neural networks in spintronic memory. / Resch, Salonik; Khatamifard, S. Karen; Chowdhury, Zamshed Iqbal; Zabihi, Masoud; Zhao, Zhengyang; Wang, Jian Ping; Sapatnekar, Sachin S.; Karpuzcu, Ulya R.

In: ACM Transactions on Architecture and Code Optimization, Vol. 16, No. 4, A41, 10.2019.

Research output: Contribution to journalArticle

Resch, Salonik ; Khatamifard, S. Karen ; Chowdhury, Zamshed Iqbal ; Zabihi, Masoud ; Zhao, Zhengyang ; Wang, Jian Ping ; Sapatnekar, Sachin S. ; Karpuzcu, Ulya R. / PIMBALL : Binary neural networks in spintronic memory. In: ACM Transactions on Architecture and Code Optimization. 2019 ; Vol. 16, No. 4.
@article{b77a4a3260864f78853d119658f8d3d1,
title = "PIMBALL: Binary neural networks in spintronic memory",
abstract = "Neural networks span a wide range of applications of industrial and commercial significance. Binary neural networks (BNN) are particularly effective in trading accuracy for performance, energy efficiency, or hardware/ software complexity. Here, we introduce a spintronic, re-configurable in-memory BNN accelerator, PIMBALL: Processing In Memory BNN AcceL(L)erator, which allows for massively parallel and energy efficient computation. PIMBALL is capable of being used as a standard spintronic memory (STT-MRAM) array and a computational substrate simultaneously. We evaluate PIMBALL using multiple image classifiers and a genomics kernel. Our simulation results show that PIMBALL is more energy efficient than alternative CPU-, GPU-, and FPGA-based implementations while delivering higher throughput.",
keywords = "Binary neural networks, Computational random access memory, Non-volatile memory, Processing in memory",
author = "Salonik Resch and Khatamifard, {S. Karen} and Chowdhury, {Zamshed Iqbal} and Masoud Zabihi and Zhengyang Zhao and Wang, {Jian Ping} and Sapatnekar, {Sachin S.} and Karpuzcu, {Ulya R.}",
year = "2019",
month = "10",
doi = "10.1145/3357250",
language = "English (US)",
volume = "16",
journal = "ACM Transactions on Architecture and Code Optimization",
issn = "1544-3566",
publisher = "Association for Computing Machinery (ACM)",
number = "4",

}

TY - JOUR

T1 - PIMBALL

T2 - Binary neural networks in spintronic memory

AU - Resch, Salonik

AU - Khatamifard, S. Karen

AU - Chowdhury, Zamshed Iqbal

AU - Zabihi, Masoud

AU - Zhao, Zhengyang

AU - Wang, Jian Ping

AU - Sapatnekar, Sachin S.

AU - Karpuzcu, Ulya R.

PY - 2019/10

Y1 - 2019/10

N2 - Neural networks span a wide range of applications of industrial and commercial significance. Binary neural networks (BNN) are particularly effective in trading accuracy for performance, energy efficiency, or hardware/ software complexity. Here, we introduce a spintronic, re-configurable in-memory BNN accelerator, PIMBALL: Processing In Memory BNN AcceL(L)erator, which allows for massively parallel and energy efficient computation. PIMBALL is capable of being used as a standard spintronic memory (STT-MRAM) array and a computational substrate simultaneously. We evaluate PIMBALL using multiple image classifiers and a genomics kernel. Our simulation results show that PIMBALL is more energy efficient than alternative CPU-, GPU-, and FPGA-based implementations while delivering higher throughput.

AB - Neural networks span a wide range of applications of industrial and commercial significance. Binary neural networks (BNN) are particularly effective in trading accuracy for performance, energy efficiency, or hardware/ software complexity. Here, we introduce a spintronic, re-configurable in-memory BNN accelerator, PIMBALL: Processing In Memory BNN AcceL(L)erator, which allows for massively parallel and energy efficient computation. PIMBALL is capable of being used as a standard spintronic memory (STT-MRAM) array and a computational substrate simultaneously. We evaluate PIMBALL using multiple image classifiers and a genomics kernel. Our simulation results show that PIMBALL is more energy efficient than alternative CPU-, GPU-, and FPGA-based implementations while delivering higher throughput.

KW - Binary neural networks

KW - Computational random access memory

KW - Non-volatile memory

KW - Processing in memory

UR - http://www.scopus.com/inward/record.url?scp=85073722481&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85073722481&partnerID=8YFLogxK

U2 - 10.1145/3357250

DO - 10.1145/3357250

M3 - Article

AN - SCOPUS:85073722481

VL - 16

JO - ACM Transactions on Architecture and Code Optimization

JF - ACM Transactions on Architecture and Code Optimization

SN - 1544-3566

IS - 4

M1 - A41

ER -