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Weightless: Lossy weight encoding for deep neural network compression
Brandon Reagen · Udit Gupta · Bob Adolf · Michael Mitzenmacher · Alexander Rush · Gu-Yeon Wei · David Brooks

Fri Jul 13 09:15 AM -- 12:00 PM (PDT) @ Hall B #138

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 co-designed with weight simplification 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, named Weightless, can compress weights by up to 496x without loss of model accuracy. This results in up to a 1.51x improvement over the state-of-the-art.

Author Information

Brandon Reagen (Harvard University)
Udit Gupta (Harvard University)
Bob Adolf (Harvard University)
Michael Mitzenmacher (Harvard University)
Alexander Rush (Harvard University)
Gu-Yeon Wei
David Brooks (Harvard University)

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