With scaling of technology feature sizes, the share of leakage in total power consumption of digital systems continues to grow. Conventional dynamic voltage scaling (DVS) techniques fail to accurately address the impact of scaling on system power consumption and hence, are incapable of achieving energy efficient solutions. To overcome this problem, we utilize adaptive body biasing (ABB) to adjust transistors' threshold voltage at runtime. We develop a leakage-aware compilation methodology that targets embedded processors with both DVS and ABB capabilities. Our technique has the unique advantage of jointly optimizing active and leakage energy dissipation. Considering the delay and energy penalty of switching between operating modes of the processor and under deadline constraint, our compiler improves the energy consumption of the generated code by average of 13 07% and up to 30 26% at 90nm. While our technique's improvement in energy dissipation over conventional DVS is marginal (4 54%) at 130nm, the average improvement continues to grow to 7 8%, 15 94% and 29 56% for 90nm, 65nm and 45 technology nodes, respectively.
We present a compilation technique that targets realtime applications running on embedded processors with combined dynamic voltage scaling (DVS) and adaptive body biasing (ABB) capabilities. Considering the delay and energy penalty of switching between operating modes of the processor, our compiler judiciously inserts mode switch instructions in selected locations of the code and generates executable binary that is guaranteed to meet the deadline constraint. More importantly, our algorithm runs very fast and comes reasonably close to the theoretical limit of energy optimization using DVS+ABB. At 65 nm technology, we improve the energy dissipation of the generated code by an average of 11 4% under deadline constraints. While our technique's improvement in energy dissipation over conventional DVS is marginal (3%) at 130nm, the average improvement continues to grow to 4 7%, 8 8% and 15 4% for 90nm, 65nm and 45nm technology nodes, respectively. Compared to a recent ILP-based competitor, we improve the runtime by more than three orders of magnitude, while producing improved results.
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