IBM boosts AI chip speed, bringing deep learning to the edge

IBM is unveiling new hardware that brings power efficiency and improved training times to artificial intelligence (AI) projects this week at the International Electron Devices Meeting (IEDM) and the Conference on Neural Information Processing Systems (NeurIPS), with 8-bit precision for both their analog and digital chips for AI. Over the last decade, computing performance for AI has improved at a rate of 2.5x per year, due in part to the use of GPUs to accelerate deep learning tasks, the company noted in a press release. However, this improvement is not sustainable, as most of the potential performance from this design model--a general-purpose computing solution tailored to AI--will not be able to keep pace with hardware designed exclusively for AI training and development. Per the press release, "Scaling AI with new hardware solutions is part of a wider effort at IBM Research to move from narrow AI, often used to solve specific, well-defined tasks, to broad AI, which reaches across disciplines to help humans solve our most pressing problems." While traditional computing has been in a decades-long path of increasing address width--with most consumer, professional, and enterprise-grade hardware using 64-bit processors--AI is going the opposite direction.