Fair and Comprehensive Benchmarking of Machine Learning Processing Chips

Geoffrey W. Burr; SukHwan Lim; Boris Murmann; Rangharajan Venkatesan; Marian Verhelst

doi:10.1109/MDAT.2021.3063366

IEEE Design and Test

Review

31 May 2022

Fair and Comprehensive Benchmarking of Machine Learning Processing Chips

View publication

Abstract

This article provides an overview of benchmarking strategies at a different set of abstraction levels and related best practices in each, while working to also give an overall view of impact.

Paper

Density implications of shift compensation postprocessing in holographic storage systems

Laure Menetrier, Geoffrey W. Burr

Applied Optics

Conference paper

Accelerating Deep Neural Networks with Analog Memory Devices

Stefano Ambrogio, Pritish Narayanan, et al.

AICAS 2020

Conference paper

Large-scale neural networks implemented with non-volatile memory as the synaptic weight element: Comparative performance analysis (accuracy, speed, and power)

Geoffrey W. Burr, Pritish Narayanan, et al.

IEDM 2015

Conference paper

Impact of Phase-Change Memory Drift on Energy Efficiency and Accuracy of Analog Compute-in-Memory Deep Learning Inference (Invited)

Martin M. Frank, Ning Li, et al.

IRPS 2023

View all publications

Abstract

Related

Density implications of shift compensation postprocessing in holographic storage systems

Accelerating Deep Neural Networks with Analog Memory Devices

Large-scale neural networks implemented with non-volatile memory as the synaptic weight element: Comparative performance analysis (accuracy, speed, and power)

Impact of Phase-Change Memory Drift on Energy Efficiency and Accuracy of Analog Compute-in-Memory Deep Learning Inference (Invited)