STAR: A Mixed Analog Stochastic In-DRAM Convolutional Neural Network Accelerator

Salma Afifi; Ishan Thakkar; Sudeep Pasricha

doi:10.1109/MDAT.2024.3447580

STAR: A Mixed Analog Stochastic In-DRAM Convolutional Neural Network Accelerator

Salma Afifi
, Ishan Thakkar
, Sudeep Pasricha

Research output: Contribution to journal › Article › peer-review

1 Scopus citations

Abstract

Editor's notes: This article addresses the optimization of data movement in accelerating machine learning workloads, one of the most critical issues of state-ofthe- art computing platforms. It presents a novel in-DRAM accelerator for convolutional neural networks using mixed analog-stochastic optimizations and shows significant energy-efficiency improvements. - Umit Ogras, University of Wisconsin, USA.

Original language	English
Pages (from-to)	47-55
Number of pages	9
Journal	IEEE Design and Test
Volume	42
Issue number	1
DOIs	https://doi.org/10.1109/MDAT.2024.3447580
State	Published - 2025

Bibliographical note

Publisher Copyright:
© 2013 IEEE.

Keywords

convolution neural networks
in-DRAM processing
stochastic computing

ASJC Scopus subject areas

Software
Hardware and Architecture
Electrical and Electronic Engineering

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/MDAT.2024.3447580

Cite this

@article{fc4c605caa344ccabb8989a2008c994e,

title = "STAR: A Mixed Analog Stochastic In-DRAM Convolutional Neural Network Accelerator",

abstract = "Editor's notes: This article addresses the optimization of data movement in accelerating machine learning workloads, one of the most critical issues of state-ofthe- art computing platforms. It presents a novel in-DRAM accelerator for convolutional neural networks using mixed analog-stochastic optimizations and shows significant energy-efficiency improvements. - Umit Ogras, University of Wisconsin, USA.",

keywords = "convolution neural networks, in-DRAM processing, stochastic computing",

author = "Salma Afifi and Ishan Thakkar and Sudeep Pasricha",

note = "Publisher Copyright: {\textcopyright} 2013 IEEE.",

year = "2025",

doi = "10.1109/MDAT.2024.3447580",

language = "English",

volume = "42",

pages = "47--55",

number = "1",

}

TY - JOUR

T1 - STAR

T2 - A Mixed Analog Stochastic In-DRAM Convolutional Neural Network Accelerator

AU - Afifi, Salma

AU - Thakkar, Ishan

AU - Pasricha, Sudeep

PY - 2025

Y1 - 2025

N2 - Editor's notes: This article addresses the optimization of data movement in accelerating machine learning workloads, one of the most critical issues of state-ofthe- art computing platforms. It presents a novel in-DRAM accelerator for convolutional neural networks using mixed analog-stochastic optimizations and shows significant energy-efficiency improvements. - Umit Ogras, University of Wisconsin, USA.

AB - Editor's notes: This article addresses the optimization of data movement in accelerating machine learning workloads, one of the most critical issues of state-ofthe- art computing platforms. It presents a novel in-DRAM accelerator for convolutional neural networks using mixed analog-stochastic optimizations and shows significant energy-efficiency improvements. - Umit Ogras, University of Wisconsin, USA.

KW - convolution neural networks

KW - in-DRAM processing

KW - stochastic computing

UR - https://www.scopus.com/pages/publications/85202722158

UR - https://www.scopus.com/pages/publications/85202722158#tab=citedBy

U2 - 10.1109/MDAT.2024.3447580

DO - 10.1109/MDAT.2024.3447580

M3 - Article

AN - SCOPUS:85202722158

SN - 2168-2356

VL - 42

SP - 47

EP - 55

JO - IEEE Design and Test

JF - IEEE Design and Test

IS - 1

ER -

STAR: A Mixed Analog Stochastic In-DRAM Convolutional Neural Network Accelerator

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

UN SDGs

Access to Document

Other files and links

Fingerprint

Cite this