RAPID: Approximate Pipelined Soft Multipliers and Dividers for High Throughput and Energy Efficiency

TLDR: RAPID as mentioned in this paper is the first pipelined approximate multiplier and divider architecture, customized for FPGAs, which efficiently utilizes 6-input Look-up Tables (6-LUTs) and fast carry chains to implement Mitchell's approximate algorithms.

Abstract: The rapid updates in error-resilient applications along with their quest for high throughput have motivated designing fast approximate functional units for Field-Programmable Gate Arrays (FPGAs). Studies that proposed imprecise functional techniques are posed with three shortcomings: first, most inexact multipliers and dividers are specialized for Application-Specific Integrated Circuit (ASIC) platforms. Second, state-of-the-art (SoA) approximate units are substituted, mostly in a single kernel of a multi-kernel application. Moreover, the end-to-end assessment is adopted on the Quality of Results (QoR), but not on the overall gained performance. Finally, existing imprecise components are not designed to support a pipelined approach, which could boost the operating frequency/throughput of, e.g., division-included applications. In this paper, we propose RAPID, the first pipelined approximate multiplier and divider architecture, customized for FPGAs. The proposed units efficiently utilize 6-input Look-up Tables (6-LUTs) and fast carry chains to implement Mitchell's approximate algorithms. Our novel error-refinement scheme not only has negligible overhead over the baseline Mitchell's approach but also boosts its accuracy to 99.4% for arbitrary size of multiplication and division. Experimental results demonstrate the efficiency of the proposed pipelined and non-pipelined RAPID multipliers and dividers over accurate counterparts. Moreover, the end-to-end evaluations of RAPID, deployed in three multi-kernel applications in the domains of bio-signal processing, image processing, and moving object tracking for Unmanned Air Vehicles (UAV) indicate up to 45% improvements in area, latency, and Area-Delay-Product (ADP), respectively, over accurate kernels, with negligible loss in QoR.