Agile: A Learning-Enabled Power and Performance-Efficient Network-on-Chip Design

Abstract

A number of techniques to achieve power-efficient Network-on-Chips (NoCs) have been proposed, two of which are power-gating and dynamic voltage and frequency scaling (DVFS). Power-gating reduces static power, and DVFS reduces dynamic power. With the goal of reducing both static and dynamic power, it is intuitive to simultaneously deploy both techniques. However, we observe that the straightforward combination of power-gating and DVFS can result in reduced power benefits and degraded performance. In this article, we uniquely combine power-gating and DVFS with the aim of maximizing the NoC power savings and improving performance. The proposed NoC design, called Agile, consists of several architectural designs and a reinforcement learning (RL) based control policy to mitigate the negative effects induced by the combined power-gating and DVFS. Specifically, a simple bypass switch is deployed to maintain network connectivity, avoiding frequently waking up the powered-off router. An optimized pipeline can simplify pipeline stages of the bypass switch to reduce network latency. Reversible link channel buffers can be dynamically allocated to where they are needed to improve throughput. In addition, the RL control policy predicts NoC traffic and decides optimal power-gating decisions, voltage/frequency levels and NoC architecture configurations at runtime. Furthermore, we explore the use of an artificial neural network (ANN) to efficiently reduce the area overhead of implementing RL. We evaluate our design using the PARSEC benchmarks suite. The full system simulation results show that the proposed design improves the overall power savings by up to 58 percent while improving the performance up to 11 percent as compared to state-of-the-art designs. The ANN-based RL implementation and bypass switch incur nominal area overhead of 5 percent, as compared to a conventional router.