LEAD: Learning-enabled Energy-Aware Dynamic Voltage/frequency scaling in NoCs
LEAD: Learning-enabled Energy-Aware Dynamic Voltage/frequency scaling in NoCs
复制标题
LEAD：NoC 中支持学习的能量感知动态电压/频率调节
DOI：
--
复制
发表时间：
2018
期刊：
影响因子：
--
通讯作者：
A. Louri
中科院分区：
文献类型：
--
作者：
Mark Clark;Razvan C. Bunescu;Avinash Karanth Kodi;A. Louri
芯片上的网络（NOC）是多层处理器的互连结构，因为它们优于传统的公共汽车和横梁，而NOC的跨度也提供了几种优势。缩放（DVF）是一种流行的技术，可以节省动态能量，但可能会导致吞吐量的损失。铅 - 启用学习能源的动态电压/NOC体系结构的频率缩放，其中我们使用机器学习技术以降低的间接费用使能量绩效的权衡降低。并提供各种电压/频率对（模式）。允许使用PARSEC和SPLASH-2基准在4×4浓缩网状结构上使用PARSEC和SPLASH-2基准，允许在较精细的粒度上进行能量管理。并且没有延迟增加。
Network on Chips (NoCs) are the interconnect fabric of choice for multicore processors due to their superiority over traditional buses and crossbars in terms of scalability. While NoC’s offer several advantages, they still suffer from high static and dynamic power consumption. Dynamic Voltage and Frequency Scaling (DVFS) is a popular technique that allows dynamic energy to be saved, but it can potentially lead to loss in throughput. In this paper, we propose LEAD - Learning-enabled Energy-Aware Dynamic voltage/frequency scaling for NoC architectures wherein we use machine learning techniques to enable energy-performance trade-offs at reduced overhead cost. LEAD enables a proactive energy management strategy that relies on an offline trained regression model and provides a wide variety of voltage/frequency pairs (modes). LEAD groups each router and the router’s outgoing links locally into the same V/F domain, allowing energy management at a finer granularity without additional timing complications and overhead. Our simulation results using PARSEC and Splash-2 benchmarks on a 4 × 4 concentrated mesh architecture show an average dynamic energy savings of 17% with a minimal loss of 4% in throughput and no latency increase.