CoOAx: Correlation-aware Synthesis of FPGA-based Approximate Operators

Abstract

The run-time reconfigurability and high parallelism offered by Field Programmable Gate Arrays (FPGAs) make them an attractive choice for implementing hardware accelerators for Machine Learning (ML) algorithms. In the quest for designing efficient FPGA-based hard-ware accelerators for ML algorithms, the inherent error-resilience of ML algorithms can be exploited to implement approximate hard-ware accelerators to trade the output accuracy with better over-all performance. As multiplication and addition are the two main arithmetic operations in ML algorithms, most state-of-the-art approximate accelerators have considered approximate architectures for these operations. However, these works have mainly considered the exploration and selection of approximate operators from an existing set of operators. To this end, we provide an efficient methodology for synthesizing and implementing novel approximate operators. Specifically, we propose a novel operator synthesis approach that supports multiple operator algorithms to provide new approximate multiplier and adder designs for AI inference applications. We report up to 27% and 25% lower power than state-of-the-art approximate designs, with equivalent error behavior, for 8-bit unsigned adders and 4-bit signed multipliers respectively. Further, we propose a correlation-aware Design Space Exploration (DSE) method that can improve the efficacy of randomized search algorithms in synthesizing novel approximate operators.