Hybrid and non-uniform quantization methods using retro synthesis data for efficient inference

Existing quantization aware training methods and post-training quantization methods attempt to compensate the quantization loss by leveraging on training data. Hence, these methods are not effective for privacy constraint applications as they are tightly coupled with training data. In contrast, this paper proposes a data-independent post-training quantization scheme that eliminates the need for training data. This is achieved by generating a faux dataset, hereafter referred to as 'Retro-Synthesis Data', from the FP32 model layer statistics and further using it for quantization. This approach outperformed state-of-the-art methods including, but not limited to, ZeroQ and DFQ on models with and without Batch-Normalization layers for 8, 6, and 4 bit precisions on ImageNet and CIFAR-10 datasets. We also introduced two futuristic variants of posttraining quantization methods namely 'Hybrid Quantization' and 'Non-Uniform Quantization'. The Hybrid Quantization scheme determines the sensitivity of each layer for per-tensor & perchannel quantization, and thereby generates hybrid quantized models that are '10 to 20%' more efficient in inference time while achieving the same or better accuracy as compared to perchannel quantization scheme. Also, this method outperformed FP32 accuracy when applied for ResNet-18, and ResNet-50 models on the ImageNet dataset. In the proposed Non-Uniform Quantization scheme, the weights are grouped into different clusters and these clusters are assigned with a varied number of quantization steps depending on the number of weights and their ranges in the respective cluster. This method resulted in '1%' accuracy improvement against state-of-the-art methods on the ImageNet dataset.