Scalable stochastic-computing accelerator for convolutional neural networks
Scalable stochastic-computing accelerator for convolutional neural networks
复制标题
用于卷积神经网络的可扩展随机计算加速器
DOI：
10.1109/aspdac.2017.7858405
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发表时间：
2017
期刊：
影响因子：
--
通讯作者：
Kiyoung Choi
中科院分区：
文献类型：
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作者：
H. Sim;Dong Nguyen;Jongeun Lee;Kiyoung Choi
随机计算（SC）是一种替代设计范式，特别适用于成本至关重要的应用。SC已经被应用于神经网络，因为神经网络以其高计算复杂度而闻名。然而，以前在这一领域的工作存在严重的局限性，例如完全并行的架构假设，这使得它们无法适用于最近的卷积神经网络或ConvNets。本文提出了ConvNets的第一个SC架构，证明了其可行性，并详细分析了实现开销。我们的SC-ConvNet是SC和传统二进制设计之间的混合，这与早期的基于SC的神经网络有显著的不同。虽然这看起来像是一种妥协，但它是一种新的功能，需要大规模支持现代ConvNets，通常有许多大型层。我们提出的架构还具有混合层组成，这有助于实现非常高的识别精度。我们的详细评估结果涉及功能仿真和RTL合成，表明SC-ConvNets确实与传统的二进制设计竞争，即使不考虑SC的固有错误恢复能力。
Stochastic Computing (SC) is an alternative design paradigm particularly useful for applications where cost is critical. SC has been applied to neural networks, as neural networks are known for their high computational complexity. However previous work in this area has critical limitations such as the fully-parallel architecture assumption, which prevent them from being applicable to recent ones such as convolutional neural networks, or ConvNets. This paper presents the first SC architecture for ConvNets, shows its feasibility, with detailed analyses of implementation overheads. Our SC-ConvNet is a hybrid between SC and conventional binary design, which is a marked difference from earlier SC-based neural networks. Though this might seem like a compromise, it is a novel feature driven by the need to support modern ConvNets at scale, which commonly have many, large layers. Our proposed architecture also features hybrid layer composition, which helps achieve very high recognition accuracy. Our detailed evaluation results involving functional simulation and RTL synthesis suggest that SC-ConvNets are indeed competitive with conventional binary designs, even without considering inherent error resilience of SC.