📝 SummarXiv

Abstract

Transprecision computing (TC) is a promising approach for energy-efficient machine learning (ML) computation on resource-constrained platforms. This work presents a novel ASIC design of a Transprecision Arithmetic and Logic Unit (TALU) that can support multiple number formats: Posit, Floating Point (FP), and Integer (INT) data with variable bitwidth of 8, 16, and 32 bits. Additionally, TALU can be reconfigured in runtime to support TC without overprovisioning the hardware. Posit is a new number format, gaining traction for ML computations, producing similar accuracy in lower bitwidth than FP representation. This paper thus proposes a novel algorithm for decoding Posit for energy-efficient computation. TALU implementation achieves a 54.6x reduction in power consumption and 19.8x reduction in the area as compared to a state-of-the-art unified MAC unit (UMAC for Posit and FP computation. Experimental results on an ML compute kernel executed on a Vector Processor of TALUs integrated with a RISC-V processor achieves about 2x improvement in energy efficiency and similar throughput as compared to a state-of-the-art TC-based vector processor.