Fast Power Estimation for Automatic Instruction-Set Selection

Recent research in the area of application specific instruction-set processors (ASIPs) has focused on the automatic selection of a custom instruction-set based on a high-level description of the application. Automatic instruction-set selection is typically comprised of instruction selection and instruction enumeration. During instruction enumeration, candidate instructions are identified using a simple cost function that minimizes the total number of operations in each basic block of the application while also adhering to the micro-architectural constraints of the ASIP. Existing methods indirectly account for power by using the above mentioned cost function and relying on the assumption that fewer operations will always reduce power. This approach is generally taken because power estimation is time-consuming. In this paper, we directly estimate the power dissipation of a custom instruction by using a simple yet effective probabilistic approach based on probability distributions of the input Hamming distance. Results indicate that our approach can estimate the power dissipation incurred by a custom instruction to within 12% of the value reported by PrimePower.