Getting the AI you want on the infrastructure you know: 2 deep-dive case studies of AI on CPU
Thanks to new software optimizations and built-in hardware acceleration, the Intel CPUs on which the world runs have never been more performant for running AI applications than they are today—and it’s only getting better. Kushal Datta specializes in optimizing AI applications on CPUs; hear two of his latest customer success stories and get the details behind the technical collaboration that led to incredible performance for AI on CPU.
What you'll learn
- Learn how Xeon can accelerate large model use cases
Kushal Datta is a senior research scientist in the Artificial Intelligence Products Group at Intel. He specializes in accelerating deep learning training and inference on Intel architecture. His noteworthy achievements are the optimization of multiscale CNN training time on Multi-Node Xeon and INT8/VNNI quantization of the transformer model. Previously, he contributed to Intel Graph Analytics Toolkit, Genomics Analytics Toolkit-4.0 (from Broad Institute), and TileDB (a multidimensional array store). He earned his PhD in ECE from the University of North Carolina at Charlotte where he created a cycle-accurate microarchitecture simulator called Casper, which was awarded the best contribution to the OpenSPARC Community Project. His doctoral dissertation used statistical machine learning and Casper to improve power efficiency of simultaneous multi-threading SPARCV9 many core microarchitectures.
Diversity and Inclusion Sponsor
Premier Exhibitor Plus
R & D and Innovation Track Sponsor
For conference registration information and customer service
For more information on community discounts and trade opportunities with O’Reilly conferences
For information on exhibiting or sponsoring a conference
For media/analyst press inquires