tinyML Talks: Low Precision Inference and Training for Deep Neural Networks

Low Precision Inference and Training for Deep Neural Networks”

Philip Leong
Chief Technology Officer
CruxML Pty
Professor
Computer Systems in the School of Electrical and Information Engineering
University of Sydney

In this talk we present Block Minifloat (BM) arithmetic, a parameterised minifloat format which is optimised for low-precision deep learning applications. While standard floating-point representations have two degrees of freedom, the exponent and mantissa, BM exposes an additional exponent bias allowing the range of a block to be controlled. Results for inference, training and transfer learning using 4-8 bit precision which achieve similar accuracy to floating point will be presented.

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