A Scalable Body Bias Optimization Method Toward Low-Power CGRAs

Body biasing is one of the critical techniques to realize more energy-efficient computing with reconfigurable devices, such as coarse-grained reconfigurable architectures. Its benefit depends on the control granularity, whereas fine-grained control makes it challenging to find the best body bias voltage for each domain due to the complexity of the optimization problem. This work reformulates the optimization problem and introduces continuous relaxation to solve it faster than previous work based on an integer linear program. Experimental result shows the proposed method can solve the problem within 0.5 s for all benchmarks in any conditions. For a middle-class problem, up to 5.65x speedup and a geometric mean of 2.06x speedup are demonstrated compared to the previous method with negligible loss of accuracy. Besides, we explore finer body bias control considering the power- and area-overhead of an on-chip body bias generator and suggest the most reasonable design saves 66% of energy consumption.