High-Level Synthesis of Approximate Hardware under Joint Precision and Voltage Scaling

Seogoo Leea, Lizy K. Johnb and Andreas Gerstlauerc
Department of Electrical and Computer Engineering, The University of Texas at Austin.


In recent years, approximate computing has emerged as a promising approach to trade off quality of computed outputs for energy savings. In this paper, we present an approximate high-level synthesis (AHLS) approach that outputs a quality-energy optimized register-transfer-level implementation from an accurate high-level C description. Existing AHLS work only considers switching activity for energy savings under hardware approximations. By contrast, we aim to provide a general AHLS solution that also considers voltage scaling given a reduced processing time. To maximize voltage and associated energy reductions, we include both operation-level approximations by bit rounding and more aggressive operation eliminations as approximation techniques. Optimally exploiting scaling opportunities under such approximations requires tight interaction with scheduling tasks. We address this problem by combining an optimization pass that estimates the scheduling impact of approximations with fast yet accurate quality-energy models and an efficient optimization solver to find near-optimal solutions constructively. Results show that when considering voltage scaling, up to 24.5% higher energy savings can be achieved compared to approaches that only consider switching activity. Our heuristic solver is able to find solutions within 0.1% of average energy savings compared to an exhaustive search, all while being up to 1,400 38 215; faster than simulation-based methods.

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