Fixed Point Quantization of Deep Convolutional Networks
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 computation and model storage resources. Fixed point implementation of DCNs has the potential to alleviate some of these complexities and facilitate potential deployment on embedded hardware. In this paper, we propose a quantizer design for fixed point implementation of DCNs. We formulate and solve an optimization problem to identify optimal fixed point bit width allocation across DCN layers. Our experiments show that in comparison to equal bitwidth settings, the fixed point DCNs with optimized bit width allocation offer 20%reduction in the model size without any loss in accuracy on CIFAR 10 benchmark. 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.
ICML