Efficient Neural Compression with Inference-time Decoding
This work addresses memory efficiency for edge deployment of neural networks, representing an incremental improvement over existing mixed precision methods.
The paper tackles the problem of neural network compression for edge deployment by addressing the accuracy loss below a certain bitwidth, introducing a method that combines mixed precision, zero-point quantization, and entropy coding to push compression beyond the 1-bit frontier with less than 1% accuracy drop on ImageNet.
This paper explores the combination of neural network quantization and entropy coding for memory footprint minimization. Edge deployment of quantized models is hampered by the harsh Pareto frontier of the accuracy-to-bitwidth tradeoff, causing dramatic accuracy loss below a certain bitwidth. This accuracy loss can be alleviated thanks to mixed precision quantization, allowing for more flexible bitwidth allocation. However, standard mixed precision benefits remain limited due to the 1-bit frontier, that forces each parameter to be encoded on at least 1 bit of data. This paper introduces an approach that combines mixed precision, zero-point quantization and entropy coding to push the compression boundary of Resnets beyond the 1-bit frontier with an accuracy drop below 1% on the ImageNet benchmark. From an implementation standpoint, a compact decoder architecture features reduced latency, thus allowing for inference-compatible decoding.