JOURNAL ARTICLE
DESIGN OF RISCV PROCESSOR USING VERILOG.
Published In: i-Manager's Journal on Digital Signal Processing, 2024, v. 12, n. 1. P. 15 1 of 3
Database: Applied Science & Technology Source Ultimate 2 of 3
Authored By: E., JAYA; B., MANEESHA; G., SRIRAM; G., SAI; M., SIDDHU 3 of 3
Abstract
The main goal of this paper is to develop a 32-bit pipelined processor with several clock domains based on the RISCV (open source RV32I Version 2.0) ISA. To minimize the complexity of the instruction set and speed up the execution time per instruction, a RISC (Reduced Instruction Set Computer) processor that uses less hardware than a CISC (Complex Instruction Set Computer) is used. Furthermore, this paper constructed this processor with five levels of pipelining with the aid of necessary block diagrams, and all of the processes are well described. In this paper, a RISCV processor is designed and simulated using Verilog. The design of the RISCV processor provides an alternative for software and hardware design to the computer designers as it provides free and open instruction set architecture (ISA). Besides, the designed RISCV processor will be using 5-stage pipeline techniques to improve the overall performance of the processor. This system is started by implementing several main modules, such as alu, aludec, maindec, imem, dmem, regfile, pc_mux, result_mux, pipeline register (IF/ID, ID/IEx, IEx/IMem, and IMem/IW), forwardMuxA, and forwardMuxB. Besides, a hazard unit is implemented into the design to mitigate hazard conditions. The functionality of these modules was simulated and verified by using Xilinx Vivado software. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:i-Manager's Journal on Digital Signal Processing. 2024/06, Vol. 12, Issue 1, p15
- Document Type:Article
- Subject Area:History
- Publication Date:2024
- ISSN:23217480
- DOI:10.26634/jdp.12.1.20567
- Accession Number:178863864
- Copyright Statement:Copyright of i-Manager's Journal on Digital Signal Processing is the property of i-manager Publications and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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