The large memory requirements of deep neural networks limit their deployment
and adoption on many devices. Model compression methods effectively reduce the
memory requirements of these models, usually through applying transformations
such as weight pruning or quantization. In this paper, we present a novel
scheme for lossy weight encoding which complements conventional compression
techniques. The encoding is based on the Bloomier filter, a probabilistic data
structure that can save space at the cost of introducing random errors.
Leveraging the ability of neural networks to tolerate these imperfections and
by re-training around the errors, the proposed technique, Weightless, can
compress DNN weights by up to 496x with the same model accuracy. This results
in up to a 1.51x improvement over the state-of-the-art
