Proactive Temperature Management in MPSoCs

Preventing thermal hot spots and large temperature variations on the die is critical for addressing the challenges in system reliabil-ity, performance, cooling cost and leakage power. Reactive thermal management methods, which take action after temperature reaches a given threshold, maintain the temperature below a critical level at the cost of performance, and do not address the temperature vari-ations. In this work, we propose a proactive thermal management approach, which estimates the future temperature using regression, and allocates workload on a multicore system to reduce and balance the temperature to avoid temperature induced problems. Our tech-nique reduces the hot spots and temperature variations signicantly in comparison to reactive strategies