Convolutional neural networks (CNN) have achieved major breakthroughs in
recent years. Their performance in computer vision have matched and in some
areas even surpassed human capabilities. Deep neural networks can capture
complex non-linear features; however this ability comes at the cost of high
computational and memory requirements. State-of-art networks require billions
of arithmetic operations and millions of parameters. To enable embedded devices
such as smartphones, Google glasses and monitoring cameras with the astonishing
power of deep learning, dedicated hardware accelerators can be used to decrease
both execution time and power consumption. In applications where fast
connection to the cloud is not guaranteed or where privacy is important,
computation needs to be done locally. Many hardware accelerators for deep
neural networks have been proposed recently. A first important step of
accelerator design is hardware-oriented approximation of deep networks, which
enables energy-efficient inference. We present Ristretto, a fast and automated
framework for CNN approximation. Ristretto simulates the hardware arithmetic of
a custom hardware accelerator. The framework reduces the bit-width of network
parameters and outputs of resource-intense layers, which reduces the chip area
for multiplication units significantly. Alternatively, Ristretto can remove the
need for multipliers altogether, resulting in an adder-only arithmetic. The
tool fine-tunes trimmed networks to achieve high classification accuracy. Since
training of deep neural networks can be time-consuming, Ristretto uses highly
optimized routines which run on the GPU. This enables fast compression of any
given network. Given a maximum tolerance of 1%, Ristretto can successfully
condense CaffeNet and SqueezeNet to 8-bit. The code for Ristretto is available.

本文介绍了一个快速自动化的 CNN 近似框架 Ristretto，其可以将 CaffeNet 和 SqueezeNet 压缩到 8 位，以此实现硬件加速，从而减少运行时间和功耗。

Ristretto：卷积神经网络的硬件取向近似

Ristretto: Hardware-Oriented Approximation of Convolutional Neural  Networks

High computational complexity hinders the widespread usage of Convolutional
Neural Networks (CNNs), especially in mobile devices. Hardware accelerators are
arguably the most promising approach for reducing both execution time and power
consumption. One of the most important steps in accelerator development is
hardware-oriented model approximation. In this paper we present Ristretto, a
model approximation framework that analyzes a given CNN with respect to
numerical resolution used in representing weights and outputs of convolutional
and fully connected layers. Ristretto can condense models by using fixed point
arithmetic and representation instead of floating point. Moreover, Ristretto
fine-tunes the resulting fixed point network. Given a maximum error tolerance
of 1%, Ristretto can successfully condense CaffeNet and SqueezeNet to 8-bit.
The code for Ristretto is available.

本文提出 Ristretto，一种模型逼近框架，可以使用固定点算术和表示来压缩卷积和全连接层的权重和输出，并且可以通过微调将结果定制到具体的硬件设备，成功地将 CaffeNet 和 SqueezeNet 压缩到 8 位。