RMAC

Abstract

Approximate computing is a way to build fast and energy efficient systems, which provides responses of good enough quality tailored for different purposes. In this paper, we propose a novel approximate floating point multiplier which efficiently multiplies two floating numbers and yields a high precision product. RMAC approximates the costly mantissa multiplication to a simple addition between the mantissa of input operands. To tune the level of accuracy, RMAC looks at the first bit of the input mantissas as well as the first N bits of the result of addition to dynamically estimate the maximum multiplication error rate. Then, RMAC decides to either accept the approximate result or re-execute the exact multiplication. Depending on the value of N, the proposed RMAC can be configured to achieve different levels of accuracy. We integrate the proposed RMAC in AMD southern Island GPU, by replacing RMAC with the existing floating point units. We test the efficiency and accuracy of the enhanced GPU on a wide range of applications including multimedia and machine learning applications. Our evaluations show that a GPU enhanced by the proposed RMAC can achieve 5.2x energydelay product improvement as opposed to GPU using conventional FPUs while ensuring less than 2% quality loss. Comparing our approach with other state-of-the-art approximate multipliers shows that RMAC can achieve 3.1x faster and 1.8x more energy efficient computations while providing the same quality of service.