In this session we present the new Intel® Deep Learning Boost (Intel® DL Boost) technology featuring integer vector neural network instructions targeting future Intel® Xeon® Scalable processors. These instructions improve throughput of multiply-add operations with int8 and int16 data types and are used to achieve performance gains in low precision convolution and matrix-matrix multiplication operations used in deep neural networks. We will also go through the 8-bit integer convolution implementation made in Intel® MKL-DNN library to demonstrate how this new instruction is used in optimized code.
Banu Nagasundaram is a product marketing manager with the Artificial Intelligence Products Group at Intel where she drives overall Intel AI products positioning, AI benchmarking strategy and acts as the technical marketer for AI products including Intel Xeon and Intel Nervana Neural Network Processors. Prior to this, Banu was Product marketing engineer with the data center group at Intel, where she supported performance marketing for Xeon Phi, Intel FPGA, and Xeon for AI. Previously, Banu was a design engineer on the exascale supercomputing research team with Intel Federal. Prior to Intel, Banu worked at Qualcomm doing design verification of mobile processors. Banu holds an MS in electrical and computer engineering from the University of Florida and is working toward an MBA at UC Berkeley’s Haas School of Business.
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