Learning-Based Quality Management for Approximate Communication in Network-on-Chips
Learning-Based Quality Management for Approximate Communication in Network-on-Chips
复制标题
DOI：
10.1109/tcad.2020.3012235
复制
发表时间：
2020-11
影响因子：
2.9
通讯作者：
Yuechen Chen;A. Louri
中科院分区：
文献类型：
--
作者：
Yuechen Chen;A. Louri
当前的多/多核系统花费大量的时间和功率传输数据，将数据跨越芯片互连。当这些系统中执行数据密集型应用程序（例如机器学习和模式识别）时，此问题就会加剧。最近的研究表明，某些数据密集型应用程序可以忍受适度的错误，从而打开一个新的设计维度，即交易结果质量以提高系统性能。在本文中，我们探讨了应用程序错误容忍度，并提出了一个近似的通信框架，以减少网络芯片网络（NOC）的功耗和延迟。提出的框架结合了一种质量控制方法和数据近似机制，以减小数据包大小，以减少网络功耗和延迟。质量控制方法会自动识别可以在传输过程中近似的误差变量，并通过分析源代码来根据应用程序的质量要求来计算其误差阈值。数据近似方法包括一种轻巧的损耗压缩方案，该方案在传输误差变量时会大大降低数据包大小。该框架会导致每个数据包的较少的燃料，并减少NOC的流量，同时保证应用程序的质量要求。我们使用Axbench基准套件使用的周期精确模拟表明，与以前的近似通信技术相比，所提出的近似通信框架可实现62％的潜伏期降低和43％的动态功率降低，同时确保95％的结果质量。
Current multi/many-core systems spend large amounts of time and power transmitting data across on-chip interconnects. This problem is aggravated when data-intensive applications, such as machine learning and pattern recognition, are executed in these systems. Recent studies show that some data-intensive applications can tolerate modest errors, thus opening a new design dimension, namely, trading result quality for better system performance. In this article, we explore application error tolerance and propose an approximate communication framework to reduce the power consumption and latency of network-on-chips (NoCs). The proposed framework incorporates a quality control method and a data approximation mechanism to reduce the packet size to decrease network power consumption and latency. The quality control method automatically identifies the error-resilient variables that can be approximated during transmission and calculates their error thresholds based on the quality requirements of the application by analyzing the source code. The data approximation method includes a lightweight lossy compression scheme, which significantly reduces packet size when the error-resilient variables are transmitted. This framework results in fewer flits in each data packet and reduces traffic in NoCs while guaranteeing the quality requirements of applications. Our cycle-accurate simulation using the AxBench benchmark suite shows that the proposed approximate communication framework achieves 62% latency reduction and 43% dynamic power reduction compared to previous approximate communication techniques while ensuring 95% result quality.