Learning-Based Quality Management for Approximate Communication in Network-on-Chips

Abstract

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.