LoBA: A Leading One Bit Based Imprecise Multiplier for Efficient Image Processing

Abstract

Several applications such as signal processing, multimedia and big data analysis exhibit computational error tolerance. This tolerance can be exploited to achieve efficient designs by sacrificing accuracy. Therefore, approximate computing presents a new design paradigm that smashes the traditional belief of error-free computations and provides efficient design with quality metrics specific to an application. The multiplication operation significantly determines the performance of the core due to the compute intensive operation. Therefore, this paper proposes a novel leading one bit based approximate (LoBA) multiplier architecture that selects k-bits from n-bit inputs (k ≤ n/4) based on leading one bit (LOB) and then computes approximate product based of these small input. The accuracy is further improved by selecting next k-bits based on LOB position and considering the partial product for computing final product. Four imprecise LoBA multipliers are presented that provide trade-off between accuracy and performance. Finally, the effectiveness of the proposed architectures is shown over the existing multipliers as standalone arithmetic unit and in the application by implementing Gaussian smoothing filters. The proposed 16-bit LoBA0 and LoBA1 designs reduce power consumption by 64.2% and 32.9%, respectively over the existing multiplier architecture.