A 22nm Coarse-Grained Reconfigurable Array with Novel Features for Machine Learning and Digital Signal Processing

Abstract

This article presents a highly compact Coarse-Grained Reconfigurable Array (CGRA) specialized for processing Digital Signal Processing (DSP) and Machine Learning (ML) operations with an outstanding micro-architectural efficiency. The CGRA consists of high functionality Processing Elements (PEs) supported by strategically placed interconnections and bidirectional data buffers made of programmable cyclic registers. These novel features accelerate large length correlations, Fast Fourier Transforms and other DSP/ML related functions. It is a resource compact CGRA with very small dimensions, i.e., 4×4 PEs and synthesized using a 22nm CMOS technology. The design of CGRA has an AMBA interface making it an industry standard coprocessor for a system-on-chip. The novelty presented in this paper is an accepted United States patent.