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RC-Based Temperature Prediction Scheme for Proactive Dynamic Thermal Management in Throttle-Based 3D NoCs

机译:基于节流的3D NoC中主动动态热管理的基于RC的温度预测方案

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The three-dimensional Network-on-Chip (3D NoC) has been proposed to solve the complex on-chip communication issues in multicore systems using die stacking in recent days. Because of the larger power density and the heterogeneous thermal conductance in different silicon layers of 3D NoC, the thermal problems of 3D NoC become more exacerbated than that of 2D NoC and become a major design constraint for a high-performance system. To control the system temperature under a certain thermal limit, many Dynamic Thermal Managements () have been proposed. Recently, for emergent cooling, the full throttling scheme is usually employed as the system temperature reaches the alarming level. Hence, the conventional reactive suffers from significant performance impact because of the pessimistic reaction. In this paper, we propose a throttle-based proactive () scheme to predict the future temperature through a new -based temperature prediction () model. The model can precisely predict the temperature with heterogeneous workload assignment with low constant computational complexity. Based on the predictive temperature, the proposed scheme will assign the suitable clock frequency for each node of the NoC system to perform early temperature control through power budget distribution. Based on the experimental results, compared with the conventional reactive throttled-based , the scheme can help to reduce 11.4∼80.3 percent fully throttled nodes and improves the network throughput by around 1.5∼211.8 percent.
机译:近年来,人们提出了三维片上网络(3D NoC)来解决多核系统中使用管芯堆叠的复杂片上通信问题。由于3D NoC的不同硅层具有更大的功率密度和不同的导热性,因此3D NoC的热问题比2D NoC更加严重,并成为高性能系统的主要设计约束。为了将系统温度控制在一定的热极限下,已经提出了许多动态热管理()。最近,对于紧急冷却,当系统温度达到警报水平时,通常采用全节流方案。因此,由于悲观的反应,常规的反应性遭受显着的性能影响。在本文中,我们提出了一种基于节流阀的主动()方案,通过新的基于温度的预测()模型来预测未来的温度。该模型可通过异构工作负载分配以较低的恒定计算复杂度精确预测温度。基于预测温度,提出的方案将为NoC系统的每个节点分配合适的时钟频率,以通过功率预算分配进行早期温度控制。根据实验结果,与传统的基于被动节流的节点相比,该方案可以减少11.4%至80.3%的完全节流节点,并将网络吞吐量提高约1.5%至211.8%。

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