SSS: self-aware system-on-chip using static-dynamic hybrid method (work-in-progress)

Gaoming Du, Shibi Ma, Zhenmin Li, Zhonghai Lu, Yiming Ouyang, M. Gao
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引用次数: 1

Abstract

Network on chip has become the de facto communication standard for multi-core or many-core system on chip, due to its scalability and flexibility. However, temperature is an important factor in NoC design, which affects the overall performance of SoC---decreasing circuit frequency, increasing energy consumption, and even shortening chip lifetime. In this paper, we propose SSS, a self-aware SoC using a static-dynamic hybrid method, which combines dynamic mapping and static mapping to reduce the hot-spots temperature for NoC based SoCs. First, we propose monitoring the thermal distribution for self-state sensoring. Then, in static mapping stage, we calculate the optimal mapping solutions under different temperature modes using discrete firefly algorithm to help self-decision making. Finally, in dynamic mapping stage, we achieve dynamic mapping through configuring NoC and SoC sentient unit for self-optimizing. Experimental results show SSS can reduce the peak temperature by up to 30.64%. FPGA prototype shows the effectiveness and smartness of SSS in reducing hot-spots temperature.
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SSS:采用静动态混合方法的自感知片上系统(在研)
片上网络由于其可扩展性和灵活性,已成为多核或多核片上系统事实上的通信标准。然而,温度是NoC设计中的一个重要因素,它会影响SoC的整体性能——降低电路频率,增加能耗,甚至缩短芯片寿命。在本文中,我们提出了一种自感知SoC SSS,该SoC采用静态动态混合方法,将动态映射和静态映射相结合,以降低基于NoC的SoC的热点温度。首先,我们提出了监测热分布的自状态传感器。然后,在静态映射阶段,我们利用离散萤火虫算法计算不同温度模式下的最优映射解,以帮助自我决策。最后,在动态映射阶段,通过配置NoC和SoC感知单元进行自优化,实现动态映射。实验结果表明,SSS可将峰值温度降低30.64%。FPGA样机验证了SSS在降低热点温度方面的有效性和智能性。
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