Lecture

While deep artificial neural networks have provided breakthroughs in AI, these gains come with heavy compute and data demands relative to their biological counterparts. Neuromorphic computing aims to narrow this gap by drawing inspiration from the form and function of biological neural circuits. The past years have seen significant progress in neuromorphic computing research, with chips like Intel’s Loihi demonstrating, for the first time, compelling quantitative gains over a range of workloads.

IEEE-Affiliated Group Name: IEEE Circuits and Systems Society

URL: https://resourcecenter.cas.ieee.org/conferences/aicas-2022/CAS2022AICAS0010.html

The true potential of AI rests on super-human learning capacity, and on the ability to selectively draw on that learning. Both of these properties – scale and selectivity – challenge, the design of AI computers and the tools used to program them. A rich pool of new ideas is emerging, driven by a new breed of computing company. In this talk, the creation of the Intelligence Processing Unit (IPU) – a new type of processor, specifically designed for AI computation,

IEEE-Affiliated Group Name: IEEE Circuits and Systems Society

URL: https://resourcecenter.cas.ieee.org/conferences/aicas-2022/CAS2022AICAS0000.html