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Speeding Up AI With Vector Instructions | Ann Steffora Mutschler Semiconductor Engineering

By November 4, 2020November 13th, 2020No Comments

Uses, challenges and tradeoffs in working with vector engines.

A search is underway across the industry to find the best way to speed up machine learning applications, and optimizing hardware for vector instructions is gaining traction as a key element in that effort.

Vector instructions are a class of instructions that enable parallel processing of data sets. An entire array of integers or floating point numbers is processed in a single operation, eliminating the loop control mechanism typically found in processing arrays. That, in turn, improves both performance and power efficiency.

This concept works particularly well with sparse matrix operations used for those data sets, which can achieve a substantial performance boost by being vectorized, said Shubhodeep Roy Choudhury, CEO at Valtrix Systems.

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