An Area-Efficient Iterative Single-Precision Floating-Point Multiplier Architecture for FPGA

Abstract

Approximate multipliers have been widely used in critical applications, such as machine learning and multimedia, which are tolerant to approximation errors. This paper proposes a novel single-precision floating-point (SPFP) multiplication algorithm and its architecture. The proposed work approximates only one of the operands to reduce the number of logic blocks and iteratively compensates the approximation error to achieve acceptable error ranges in applications. To reduce the accuracy degradation by the single operand approximation, a rounding scheme and an operand selection scheme are additionally introduced. Compared with the widely-known previous iterative Mitchell design, our proposed SPFP multiplier design decreases the numbers of look up tables (LUTs) and flip flops (FFs) by 55% and 59% respectively, and shows two cycles shorter latency. The accuracy of our design becomes close to that of the iterative Mitchell design as the number of iterations increases, and it always meets the error tolerance of 1% when the number of iterations is four.