  Existing quantization aware training methods attempt to compensate for the
quantization loss by leveraging on training data, like most of the
post-training quantization methods, and are also time consuming. Both 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 post-training
quantization methods namely Hybrid Quantization and Non-Uniform Quantization
