  Neural network quantization is a promising compression technique to reduce
memory footprint and save energy consumption, potentially leading to real-time
inference. However, there is a performance gap between quantized and
full-precision models. To reduce it, existing quantization approaches require
high-precision INT32 or full-precision multiplication during inference for
scaling or dequantization. This introduces a noticeable cost in terms of
memory, speed, and required energy. To tackle these issues, we present F8Net, a
novel quantization framework consisting of only fixed-point 8-bit
multiplication. To derive our method, we first discuss the advantages of
fixed-point multiplication with different formats of fixed-point numbers and
study the statistical behavior of the associated fixed-point numbers. Second,
based on the statistical and algorithmic analysis, we apply different
fixed-point formats for weights and activations of different layers. We
introduce a novel algorithm to automatically determine the right format for
each layer during training. Third, we analyze a previous quantization algorithm
-- parameterized clipping activation (PACT) -- and reformulate it using
fixed-point arithmetic. Finally, we unify the recently proposed method for
quantization fine-tuning and our fixed-point approach to show the potential of
our method. We verify F8Net on ImageNet for MobileNet V1/V2 and ResNet18/50.
Our approach achieves comparable and better performance, when compared not only
to existing quantization techniques with INT32 multiplication or floating-point
arithmetic, but also to the full-precision counterparts, achieving
state-of-the-art performance.

Computer Vision and Pattern Recognition;Artificial Intelligence;Hardware Architecture;Machine Learning;Neural and Evolutionary Computing