Convolutional Neural Networks (CNNs) have emerged as a fundamental technology
for machine learning. High performance and extreme energy efficiency are
critical for deployments of CNNs in a wide range of situations, especially
mobile platforms such as autonomous vehicles, cameras, and electronic personal
assistants. This paper introduces the Sparse CNN (SCNN) accelerator
architecture, which improves performance and energy efficiency by exploiting
the zero-valued weights that stem from network pruning during training and
zero-valued activations that arise from the common ReLU operator applied during
inference. Specifically, SCNN employs a novel dataflow that enables maintaining
the sparse weights and activations in a compressed encoding, which eliminates
unnecessary data transfers and reduces storage requirements. Furthermore, the
SCNN dataflow facilitates efficient delivery of those weights and activations
to the multiplier array, where they are extensively reused. In addition, the
accumulation of multiplication products are performed in a novel accumulator
array. Our results show that on contemporary neural networks, SCNN can improve
both performance and energy by a factor of 2.7x and 2.3x, respectively, over a
comparably provisioned dense CNN accelerator.

该论文介绍了一种名为 Sparse CNN 的卷积神经网络加速器体系结构，它可以通过利用 CNN 训练期间的网络修剪所产生的零值权重以及推理期间常见的 ReLU 算子产生的零值激活来提高性能和能源效率，从而减少数据传输和存储要求，并通过一种新型数据流方式有效地传递权重和激活值到乘法器阵列。该加速器在现代神经网络上可以将性能和能源效率分别提升 2.7 倍和 2.3 倍。