Hybrid dynamic energy and thermal management in heterogeneous embedded multiprocessor SoCs

Heterogeneous multiprocessor system-on-chips (MPSoCs) which consist of cores with various power and performance characteristics can customize their configuration to achieve higher performance per Watt. On the other hand, inherent imbalance in power densities across MPSoCs leads to non-uniform temperature distributions, which affect performance and reliability adversely. In addition, managing temperature might result in conflicting decisions with achieving higher energy efficiency. In this work, we propose a joint thermal and energy management technique specifically designed for heterogeneous MPSoCs. Our technique identifies the performance demands of the current workload. By utilizing job scheduling and voltage/frequency scaling dynamically, we meet the desired performance while minimizing the energy consumption and the thermal imbalance. In comparison to performance-aware policies such as load balancing, our technique simultaneously reduces the thermal hot spots, temperature gradients, and energy consumption significantly.