A 13.7 TFLOPS/W Floating-point DNN Processor using Heterogeneous Computing Architecture with Exponent-Computing-in-Memory

Abstract

An energy-efficient floating-point DNN training processor is proposed with heterogenous bfloat16 computing architecture using exponent computing-in-memory (CIM) and mantissa processing engine. Mantissa free exponent calculation enables pipelining of exponent and mantissa operation for heterogenous bfloat16 computing while reducing MAC power by 14.4 %. 6T SRAM exponent computing-in-memory with bitline charge reusing reduces memory access power by 46.4 %. The processor fabricated in 28 nm CMOS technology and occupies 1.62×3.6 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> die area. It achieves 13.7 TFLOPS/W energy efficiency which is 274× higher than the previous floating-point CIM processor.