KIP-Veröffentlichungen

Albeit their rising success and wide usage throughout industry, Deep Neural Networks are not yet widely applied in embedded edge computing devices. To bridge the gap between powerful computing centers and energy efficient embedded devices, a common strategy is the utilization of modern process nodes to implement efficient digital edge accelerators. Within this talk, BrainScaleS-2 will be presented as an alternative approach: an analog neural network accelerator manufactured in an affordable 65nm CMOS process. This hybrid neuromorphic system embeds a powerful acceleration engine for brain-inspired spiking neural networks, which can additionally be used as an analog multiply-accumulate unit for the computation of classical machine learning tasks. It therefore allows co-designed artificial and spiking neural network implementations to run on a single microchip. This talk will introduce BrainScaleS-2 as a highly scalable, power efficient neural network accelerator and present experimental results from an initial prototype system.