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 percent latency reduction and 43 percent dynamic power reduction compared to previous approximate communication techniques while ensuring 95 percent result quality.

中文翻译：

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片上网络的近似通信框架

当前的多核/多核系统花费大量时间和电力通过片上互连传输数据。当在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，这个问题会更加严重。最近的研究表明，一些数据密集型应用程序可以容忍适度的错误，从而开辟了一个新的设计维度，即为了更好的系统性能而交易结果质量。在本文中，我们探讨了应用程序容错并提出了一个近似的通信框架，以降低片上网络 (NoC) 的功耗和延迟。所提出的框架结合了质量控制方法和数据近似机制，以减少数据包大小以降低网络功耗和延迟。质量控制方法自动识别在传输过程中可以近似的容错变量，并通过分析源代码根据应用程序的质量要求计算它们的错误阈值。数据近似方法包括轻量级有损压缩方案，当传输容错变量时，该方案显着减小了数据包大小。该框架可减少每个数据包中的飞行次数并减少 NoC 中的流量，同时保证应用程序的质量要求。