№ files_lp_3_process_7_070744
Scientific article comparing energy consumption in programmable optical and electrical computing architectures, separating data transfer from computation to evaluate multiplication, addition, and inner product operations in the context of machine learning workloads.
Author: Chris Cole
Affiliation: II-VI Incorporated, 1389 Moffett Park Drive, Sunnyvale, CA 94089, USA
Year: 2021
Publisher: Optical Society of America
Publication Type: Scientific article
Access: OSA Open Access Publishing Agreement
Subject Area: Optical computing, electrical computing, energy consumption
Key Topics: Programmable computing, data transfer energy, multiplication, addition, inner product, machine learning
Scope: Energy use comparison of programmable optical and electrical computing architectures
Computing Models Analyzed: Electrical data transfer, optical data transfer, optically assisted computing
Application Context: Math-intensive applications including matrix-vector products and neural network training
Price: 8 / 10 USD
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