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

Demonstration of transformer-based ALBERT model on a 14nm analog AI inference chip

An Chen, Stefano Ambrogio, et al.

Nature Communications

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

Paper

Balancing interpixel cross talk and detector noise to optimize areal density in holographic storage systems

María-P. Bernal, Geoffrey W. Burr, et al.

Applied Optics

Conference paper

Reliability Challenges with Materials for Analog Computing

Eduard Cartier, Wanki Kim, et al.

IRPS 2019

View all publications

Abstract

Related

Demonstration of transformer-based ALBERT model on a 14nm analog AI inference chip

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

Balancing interpixel cross talk and detector noise to optimize areal density in holographic storage systems

Reliability Challenges with Materials for Analog Computing