Adaptive Block Floating-Point for Analog Deep Learning Hardware. (arXiv:2205.06287v1 [cs.LG])

Analog mixed-signal (AMS) devices promise faster, more energy-efficient deep
neural network (DNN) inference than their digital counterparts. However, recent
studies show that DNNs on AMS devices with fixed-point numbers can incur an
accuracy penalty because of precision loss. To mitigate this penalty, we
present a novel AMS-compatible adaptive block floating-point (ABFP) number
representation. We also introduce amplification (or gain) as a method for
increasing the accuracy of the number representation without increasing the bit
precision of the output. We evaluate …

analog arxiv deep learning hardware learning