RISC-V basics: The truth about custom extensions
By Marc Evans, Andes Technology
EDN | August 12, 2025
The era of universal processor architectures is giving way to workload-specific designs optimized for performance, power, and scalability. As data-centric applications in artificial intelligence (AI), edge computing, automotive, and industrial markets continue to expand, they are driving a fundamental shift in processor design.
Arguably, chipmakers can no longer rely on generalized architectures to meet the demands of these specialized markets. Open ecosystems like RISC-V empower silicon developers to craft custom solutions that deliver both innovation and design efficiency, unlocking new opportunities across diverse applications.
RISC-V, an open-source instruction set architecture (ISA), is rapidly gaining momentum for its extensibility and royalty-free licensing. According to Rich Wawrzyniak, principal analyst at The SHD Group, “RISC-V SoC shipments are projected to grow at nearly 47% CAGR, capturing close to 35% of the global market by 2030.” This growth highlights why SoC designers are increasingly embracing architectures that offer greater flexibility and specialization.
To read the full article, click here
Related Semiconductor IP
- 64 bit RISC-V Multicore Processor with 2048-bit VLEN and AMM
- 64-bit Multiprocessor with Level-2 Cache-Coherence
- 64-bit CPU Core with Level-2 Cache Controller
- 32 bit RISC-V Multicore Processor with 256-bit VLEN and AMM
- Gen#2 of 64-bit RISC-V core with out-of-order pipeline based complex
Related White Papers
- ISA optimizations for hardware and software harmony: Custom instructions and RISC-V extensions
- Case study: optimizing PPA with RISC-V custom extensions in TWS earbuds
- The Hard Truth about System-on-Chip Designs
- Creating a custom processor with RISC-V
Latest White Papers
- Attack on a PUF-based Secure Binary Neural Network
- BBOPlace-Bench: Benchmarking Black-Box Optimization for Chip Placement
- FD-SOI: A Cyber-Resilient Substrate Against Laser Fault Injection—The Future Platform for Secure Automotive Electronics
- In-DRAM True Random Number Generation Using Simultaneous Multiple-Row Activation: An Experimental Study of Real DRAM Chips
- SPAD: Specialized Prefill and Decode Hardware for Disaggregated LLM Inference