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

Evolutionary Optimization of All-Dielectric Magnetic Nanoantennas

Nicolas Bonod, Sebastien Bidault, et al.

Advanced Optical Materials

Paper

Experimental Demonstration and Tolerancing of a Large-Scale Neural Network (165 000 Synapses) Using Phase-Change Memory as the Synaptic Weight Element

Geoffrey W. Burr, Robert M. Shelby, et al.

IEEE T-ED

Conference paper

Massively-parallel FDTD simulations to address mask electromagnetic effects in Hyper-NA immersion lithography

Jaione Tirapu Azpiroz, Geoffrey W. Burr, et al.

SPIE Advanced Lithography 2008

Paper

Tutorial: Brain-inspired computing using phase-change memory devices

Abu Sebastian, Manuel Le Gallo, et al.

Journal of Applied Physics

View all publications

Abstract

Related

Evolutionary Optimization of All-Dielectric Magnetic Nanoantennas

Experimental Demonstration and Tolerancing of a Large-Scale Neural Network (165 000 Synapses) Using Phase-Change Memory as the Synaptic Weight Element

Massively-parallel FDTD simulations to address mask electromagnetic effects in Hyper-NA immersion lithography

Tutorial: Brain-inspired computing using phase-change memory devices