>
Benjamin Netanyahu Holds Middle Finger In The Face Of The U.S. And the Entire World
Doomed Dali ship's audio black box reveals multiple alarms were blaring in moments...
Hair-loss treatment found in cinnamon
Baltimore Bridge Collapse May Cost Billions, Dramatically Disrupt Supply Chains
Scientists Close To Controlling All Genetic Material On Earth
Doodle to reality: World's 1st nuclear fusion-powered electric propulsion drive
Phase-change concrete melts snow and ice without salt or shovels
You Won't Want To Miss THIS During The Total Solar Eclipse (3D Eclipse Timeline And Viewing Tips
China Room Temperature Superconductor Researcher Had Experiments to Refute Critics
5 video games we wanna smell, now that it's kinda possible with GameScent
Unpowered cargo gliders on tow ropes promise 65% cheaper air freight
Wyoming A Finalist For Factory To Build Portable Micro-Nuclear Plants
High-Speed Railway Progresses Towards 200-mph Dallas-Houston Line
27 Ft-tall 3D-printed Structure Built by New Robot | ICON's Multi-Story Robotic Construction Sys
IBM is partnering with State University of New York to develop an AI Hardware Center at SUNY Polytechnic Institute in Albany. New York will also provide a subsidy of $300 million.
The IBM Research AI Hardware Center will enable IBM and their partner ecosystem to achieve 1,000x AI performance efficiency improvement over the next decade. They will overcome current machine-learning limitations by using approximate computing with Digital AI Cores and in-memory computing with Analog AI Cores.
Approximate Computing with Digital AI Cores
The best hardware platforms for training deep neural networks (DNNs) has just moved from traditional single precision (32-bit) computations towards 16-bit precision. This is more energy efficient and uses less memory. IBM researchers have successfully trained DNNs using 8-bit floating point numbers (FP8) while fully maintaining the accuracy of deep learning models and datasets.