Title: Deep Convolutional Neural Network Inference with Floating-point Weights and Fixed-point Activations,
Abstract: Deep convolutional neural network (CNN) inference requires significant amount
of memory and computation, which limits its deployment on embedded devices. To
alleviate these problems to some extent, prior research utilize low precision
fixed-point numbers to represent the CNN weights and activations. However, the
minimum required data precision of fixed-point weights varies across different
networks and also across different layers of the same network. In this work, we
propose using floating-point numbers for representing the weights and
fixed-point numbers for representing the activations. We show that using
floating-point representation for weights is more efficient than fixed-point
representation for the same bit-width and demonstrate it on popular large-scale
CNNs such as AlexNet, SqueezeNet, GoogLeNet and VGG-16. We also show that such
a representation scheme enables compact hardware multiply-and-accumulate (MAC)
unit design. Experimental results show that the proposed scheme reduces the
weight storage by up to 36% and power consumption of the hardware multiplier by
up to 50%.