A Technique for Approximate Communication in Network-on-Chips for Image Classification
A Technique for Approximate Communication in Network-on-Chips for Image Classification
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DOI：
10.1109/tetc.2022.3162165
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发表时间：
2021-08
影响因子：
5.9
通讯作者：
Yuechen Chen;Shanshan Liu;Fabrizio Lombardi;A. Louri
中科院分区：
文献类型：
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作者：
Yuechen Chen;Shanshan Liu;Fabrizio Lombardi;A. Louri
近似是一种新兴的设计方法，用于减少许多计算应用程序中芯片通信的功耗和潜伏期。但是，现有的近似技术要么实现这些指标的适度改进，要么需要在近似后进行重新培训。由于对许多图像进行分类引入了密集的片上沟通，因此非常需要网络延迟和功耗的减少。在本文中，我们提出了一种近似通信技术（ACT），以提高图像分类应用程序的芯片通信效率。所提出的技术利用了图像分类过程的误差，以减少芯片通信的功耗和延迟，从而为图像分类提供更好的整体性能。这是通过结合新的质量控制和减少数据包大小的数据近似机制来实现的。特别是，提出的质量控制机制识别出误差变量，并根据图像分类精度自动调整变量的误差阈值。当变量传输时，提出的数据近似机制可显着降低数据包的大小。提出的技术减少了每个数据包中的FLIS数量以及片上通信，同时保持出色的图像分类精度。周期精确的模拟结果表明，与现有的近似通信技术相比，ACT在网络潜伏期减少和动态功率降低的24％方面可实现23％，分类精度损失少于0.99％。
Approximation is an emerging design methodology for reducing power consumption and latency of on-chip communication in many computing applications. However, existing approximation techniques either achieve modest improvements in these metrics or require retraining after approximation. Since classifying many images introduces intensive on-chip communication, reductions in both network latency and power consumption are highly desired. In this paper, we propose an approximate communication technique (ACT) to improve the efficiency of on-chip communications for image classification applications. The proposed technique exploits the error-tolerance of the image classification process to reduce power consumption and latency of on-chip communications, resulting in better overall performance for image classification. This is achieved by incorporating novel quality control and data approximation mechanisms that reduce the packet size. In particular, the proposed quality control mechanisms identify the error-resilient variables and automatically adjust the error thresholds of the variables based on the image classification accuracy. The proposed data approximation mechanisms significantly reduce packet size when the variables are transmitted. The proposed technique reduces the number of flits in each data packet as well as the on-chip communication while maintaining an excellent image classification accuracy. Cycle-accurate simulation results show that ACT achieves 23% in network latency reduction and 24% in dynamic power reduction as compared to existing approximate communication techniques with less than 0.99% classification accuracy loss.