Approximate Computing for On-Chip AI Acceleration: IBM Research at VLSI

Recent advances in deep learning and exponential growth in the use of machine learning across application domains have made AI acceleration critically important. IBM Research has been building a pipeline of AI hardware accelerators to meet this need. At the 2018 VLSI Circuits Symposium, we presented a multi-TeraOPS accelerator core building block that can be scaled across a broad range of AI hardware systems. This digital AI core features a parallel architecture that ensures very high utilization and efficient compute engines that carefully leverage reduced precision. Approximate computing is a central tenet of our approach to harnessing "the physics of AI", in which highly energy-efficient computing gains are achieved by purpose-built architectures, initially using digital computations and later including analog and in-memory computing.