Lightweight architectural designs of Convolutional Neural Networks (CNNs)
together with quantization have paved the way for the deployment of demanding
computer vision applications on mobile devices. Parallel to this, alternative
formulations to the convolution operation such as FFT, Strassen and Winograd,
have been adapted for use in CNNs offering further speedups. Winograd
convolutions are the fastest known algorithm for spatially small convolutions,
but exploiting their full potential comes with the burden of numerical error,
rendering them unusable in quantized contexts. In this work we propose a
Winograd-aware formulation of convolution layers which exposes the numerical
inaccuracies introduced by the Winograd transformations to the learning of the
model parameters, enabling the design of competitive quantized models without
impacting model size. We also address the source of the numerical error and
propose a relaxation on the form of the transformation matrices, resulting in
up to 10% higher classification accuracy on CIFAR-10. Finally, we propose
wiNAS, a neural architecture search (NAS) framework that jointly optimizes a
given macro-architecture for accuracy and latency leveraging Winograd-aware
layers. A Winograd-aware ResNet-18 optimized with wiNAS for CIFAR-10 results in
2.66x speedup compared to im2row, one of the most widely used optimized
convolution implementations, with no loss in accuracy.

提出了一种 Winograd 感知的卷积层形式，通过将 Winograd 变换的数值误差暴露给模型的参数学习来设计具有竞争力的量化模型，同时提出了一种 WiNAS 框架以在准确性和延迟方面联合优化给定宏结构，优化后的 ResNet-18 在 CIFAR-10 上速度提高了 2.66 倍，在不损失准确性的情况下。

寻找 Winograd 感知量化网络

Searching for Winograd-aware Quantized Networks

The Winograd or Cook-Toom class of algorithms help to reduce the overall
compute complexity of many modern deep convolutional neural networks (CNNs).
Although there has been a lot of research done on model and algorithmic
optimization of CNN, little attention has been paid to the efficient
implementation of these algorithms on embedded CPUs, which usually have very
limited memory and low power budget. This paper aims to fill this gap and
focuses on the efficient implementation of Winograd or Cook-Toom based
convolution on modern Arm Cortex-A CPUs, widely used in mobile devices today.
Specifically, we demonstrate a reduction in inference latency by using a set of
optimization strategies that improve the utilization of computational
resources, and by effectively leveraging the ARMv8-A NEON SIMD instruction set.
We evaluated our proposed region-wise multi-channel implementations on Arm
Cortex-A73 platform using several representative CNNs. The results show
significant performance improvements in full network, up to 60%, over existing
im2row/im2col based optimization techniques

本文针对移动设备上广泛使用的 ARM Cortex-A CPU，探讨了 Winograd 或 Cook-Toom 特征压缩算法在卷积神经网络上的高效实现，通过优化计算资源的利用和充分发挥 ARMv8-A NEON SIMD 指令集等策略降低了推断延迟，并在数个代表性 CNN 上进行了模型评估，结果显示相比现有的 im2row/im2col 基于优化技术，可在全网络中提高 60% 左右的性能。