An Extensive Analysis of Techniques and Developments in the Progress of RISC-V Customization and Hardware/Software Co-Design

Abstract

Co-designing hardware and software and customizing Reduced Instruction Set Computer-V (RISC-V) have become important strategies for maximizing computer systems' performance, adaptability, and resource efficiency. Architectural review, neural network-based partitioning, and reconfigurable array designs are some of the major developments in the field of co-design concepts explored in this research work. The major advances in parallel computing and automation of the design process include the Adaptive Dataflow Architecture for Processing and Training Optimization (ADAPTO) array, Configurable Tagged Memory Extension (COMET) technique, and Kahn Process Networks. Dynamic micro-decoders, unique instruction set, and vector extensions for Artificial Neural Network (ANN) optimization are a few of the important developments in this area. It comprehensively compares the performance improvement, resource usage, and design automation framework that overcomes the current constraints for the broadening of RISC-V applications to many computing domains. The paper also underlines some state-of-the-art techniques. Current innovations enable us to realize a number of new technologies, which utilize advanced techniques, such as hardware-software co-design; adaptive pipelines; and intelligent memory management, leading to greatly enhanced computation efficiency. A number of the new methods being studied for both AI and edge computing are: custom neural processors, low-power accelerators, automated high-level synthesis tools, etc., all providing alternate means to meet increasing requirements for AI and edge computing use. All of these factors contribute to improved performance, lower latencies, and optimized use of resources and therefore, place RISC-V processing architectures as being the preferred architecture for the Intelligent Computing Solutions of the future.