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Fixed Point Quantization of Deep Convolutional Networks | | Darryl D. Lin
; Sachin S. Talathi
; V. Sreekanth Annapureddy
; | | Date: |
19 Nov 2015 | | Abstract: | In recent years increasingly complex architectures for deep convolution
networks (DCNs) have been proposed to boost the performance on image
recognition tasks. However, the gains in performance have come at a cost of
substantial increase in compute resources, the model size and processing speed
of the network for training and evaluation. Fixed point implementation of these
networks has the potential to alleviate some of the burden of these additional
complexities. In this paper, we propose a quantizer design for fixed point
implementation for DCNs. We then formulate an optimization problem to identify
optimal fixed point bit-width allocation across DCN layers. We perform
experiments on a recently proposed DCN architecture for CIFAR-10 benchmark that
generates test error of less than 7%. We evaluate the effectiveness of our
proposed fixed point bit-width allocation for this DCN. Our experiments show
that in comparison to equal bit-width settings, the fixed point DCNs with
optimized bit width allocation offer >20% reduction in the model size without
any loss in performance. We also demonstrate that fine tuning can further
enhance the accuracy of fixed point DCNs beyond that of the original floating
point model. In doing so, we report a new state-of-the-art fixed point
performance of 6.78% error-rate on CIFAR-10 benchmark. | | Source: | arXiv, 1511.6393 | | Services: | Forum | Review | PDF | Favorites | |
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