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.