UNIQ: Uniform Noise Injection for the Quantization of Neural Networks
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We present a novel method for training deep neural network amenable to inference in low-precision arithmetic with quantized weights and activations. The training is performed in full precision with random noise injection emulating quantization noise. In order to circumvent the need to simulate realistic quantization noise distributions, the weight and the activation distributions are uniformized by a non-linear transformation, and uniform noise is injected. An inverse transformation is then applied. This procedure emulates a non-uniform k-quantile quantizer at inference time, which is shown to achieve state-of-the-art results for training low-precision networks on CIFAR-10 and ImageNet-1K datasets. In particular, we observe no degradation in accuracy for MobileNet and ResNet-18 on ImageNet with as low as 2-bit quantization of the activations and minimal degradation for as little as 4 bits for the weights.
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