RISC-V + AI: How the Open Instruction Set Is Reshaping On-Device Intelligent Chips

Introduction: The Technical Direction Behind Recent Investment Activity

Several chip design companies have recently gone public, with those positioning RISC-V + AI as their core strategy drawing notable market attention. At the same time, startups in quantum computing, AI for Science, and brain-computer interfaces have continued to secure funding. Looking past individual company news, these capital flows point to a shared technical judgment: open instruction sets combined with customized AI compute are becoming a reliable path forward for the chip industry.

Public information shows that relevant chip companies have maintained double-digit compound annual revenue growth in recent years. Their products span human-machine interaction, multimedia processing, interconnect and computing, extending into consumer terminals, automotive electronics, robotics, and industrial control. These business descriptions outline the typical deployment landscape for RISC-V + AI. This article does not analyze specific companies. Instead, it returns to the technology itself, examining the principles, engineering practices, and methodologies behind combining RISC-V with AI, providing developers with an actionable technical reference.

The RISC-V Technical Foundation: Why an Open ISA Matters

The ISA as a Contract Between Software and Hardware

An Instruction Set Architecture (ISA) defines the set of commands a processor can understand. It is the boundary contract between compilers, operating systems, application software, and hardware. Traditional commercial ISAs follow a licensing model: chip vendors pay license fees and royalties, and the pace of architectural evolution is controlled by the licensor. RISC-V takes a different path. Its specification is published as an open standard, and any organization or individual can implement it freely and extend it without restriction.

Modularity: A Small Core with Pluggable Extensions

The RISC-V base integer instruction set is deliberately minimal, containing only a few dozen fundamental instructions. Additional capabilities are layered on through standard extensions as needed:

• M/A/F/D/C extensions: multiplication and division, atomic operations, single/double-precision floating-point, and compressed instructions respectively
• V (vector extension): targets data-parallel computation and is key to AI acceleration
• Custom extension space: the specification reserves dedicated encoding regions, allowing vendors to add private instructions without breaking compatibility

This modular design yields two direct benefits. First, it enables cost- and power-optimized configurations for different markets, from embedded to edge to high-performance computing. Second, because AI workloads evolve quickly, vendors can rapidly introduce custom instructions for new operators without waiting for a full architectural generation change.

The Engineering Value of Open Governance

Openness is not just a business matter; it is an engineering matter. For developers, RISC-V means publicly accessible ISA documentation, toolchains that can be built from source, and abundant microarchitecture implementations from both academia and industry, facilitating teaching, verification, and secondary development. For the industry, it reduces the risk of being locked into a single controlling entity, which is especially important in long-lifecycle domains such as automotive electronics and industrial control.

Three Integration Layers for RISC-V + AI

Instruction Layer: Vector Extension and Operator Acceleration

The core computation of AI inference can be decomposed into fundamental operators such as matrix multiplication, convolution, and element-wise operations. These operators are naturally suited to data parallelism. The RISC-V vector extension adopts a vector-length agnostic design: the same vectorized code can run on low-power cores with narrow vector units or automatically achieve higher throughput on cores with wider vector units, with no software modifications required. This characteristic significantly reduces the cost of porting and maintaining AI operator libraries.

Microarchitecture Layer: Control Cores Plus Dedicated Accelerators

A common organization for AI chips in the industry is to use one or more RISC-V cores for control, scheduling, and scalar computation, while attaching dedicated matrix/convolution accelerator units to handle the primary compute load. RISC-V cores are small, configurable, and free of licensing burdens, making them well suited as the glue and scheduling role. They are also frequently used as embedded control cores inside accelerators. Furthermore, custom instructions can push high-frequency operations such as quantization, activation, and normalization into the pipeline, further reducing end-to-end latency.

System Layer: The Software Stack Determines Ecosystem Viability

Hardware is only the starting point. What truly determines usability is the software stack. Open-source compiler suites have mature support for the RISC-V backend. Open-source AI compilers can compile models exported from mainstream training frameworks to the RISC-V backend. Operator libraries, performance profiling, and simulation tools built around the vector extension are also continuously improving. For developers, understanding the compilation pipeline of model to graph optimization to operator selection/code generation to instruction-level optimization is key to successfully deploying AI on RISC-V platforms.

Deployment Paths Across Diverse Scenarios

• Consumer terminals: Human-machine interaction (voice wake-up, touch, display) and multimedia processing (audio/video codecs) are cost- and power-sensitive. The combination of small RISC-V cores and the vector extension has natural advantages here.
• Automotive electronics: Cockpit and autonomous driving domain controllers have diverse requirements. Functional safety and long supply cycles are hard constraints. An open ISA makes it easier for automakers and suppliers to deeply customize and audit implementations.
• Robotics: Real-time control (motor control, trajectory planning) coexists with on-device perception inference. Heterogeneous integration of RISC-V control cores and AI accelerators can balance determinism and compute capability.
• Industrial control: Protocol stack processing, edge quality inspection, and predictive maintenance emphasize deterministic latency and long-term stability. The configurability and royalty-free nature of RISC-V fit production-line deployment.

Engineering Practice: From Vector Intrinsics to Custom Instructions

Accelerating Basic Operators with the Vector Extension

The following example demonstrates a typical implementation of single-precision vector addition using vector extension intrinsics, following the naming conventions of mainstream open-source toolchains:

#include <riscv_vector.h>
void vec_add_f32(const float *a, const float *b, float *c, size_t n) {
    for (size_t i = 0; i < n; ) {
        // Hardware returns the number of elements that can be processed
        // in this iteration, making the code vector-length agnostic
        size_t vl = __riscv_vsetvl_e32m1(n - i);
        vfloat32m1_t va = __riscv_vle32_v_f32m1(a + i, vl);   // load
        vfloat32m1_t vb = __riscv_vle32_v_f32m1(b + i, vl);
        vfloat32m1_t vc = __riscv_vfadd_vv_f32m1(va, vb, vl); // compute
        __riscv_vse32_v_f32m1(c + i, vc, vl);                 // store
        i += vl;
    }
}

Two engineering details deserve attention in this code. First, the loop step is dynamically determined by vsetvl, so the same code can adapt to hardware implementations with different vector widths. Second, tail elements require no separate handling because vl naturally truncates the iteration, which is an error-prone point often overlooked in vectorized code.

Designing and Integrating Custom Instructions

When a specific operator, such as INT8 activation clipping, becomes a performance bottleneck, you can design a custom instruction for it. The specification reserves dedicated encoding space for vendors. The typical workflow is:

1. Hardware side: identify the custom encoding at the decode stage and route it to a coprocessing unit for computation
2. Software side: expose it to C/C++ code through inline assembly or compiler builtins
3. Verification side: validate functionality first in an instruction set simulator, then measure timing and power consumption on an FPGA prototype

An illustrative inline assembly wrapper is shown below:

/* Illustrative code: assumes the toolchain has registered the custom.relu mnemonic */
static inline int8_t clamp_relu(int8_t x, int8_t hi) {
    int8_t r;
    __asm__ volatile (
        "custom.relu %[out], %[in], %[h]"
        : [out] "=r"(r)
        : [in] "r"(x), [h] "r"(hi)
    );
    return r;
}

It is important to emphasize that custom instructions are a double-edged sword. They bring performance gains but also introduce forks in the software stack. The recommended engineering practice is to encapsulate custom instructions inside operator libraries, with upper-level algorithm code depending only on stable library interfaces, thereby isolating hardware differences within the smallest possible scope.

A General Methodology for Performance Optimization

For AI performance optimization on RISC-V platforms, the standard industry approach remains: first perform a Roofline analysis to determine whether the program is memory-bound or compute-bound. If memory-bound, prioritize optimizing data layout, tiling, and data reuse. If compute-bound, consider reduced precision such as FP16 or INT8 quantization, improving vector utilization, and enabling custom instructions. On the tooling side, performance profilers, instruction-level simulators, and cycle-accurate models form the core toolkit for locating bottlenecks.

Challenges and a Realistic Perspective

RISC-V + AI is not without obstacles. Three issues warrant ongoing attention:

1. Fragmentation risk: if vendor-specific custom extensions do not converge, software porting costs rise significantly. A pragmatic approach is to prioritize standard extensions such as the vector extension and limit custom instructions to a small number of operators with the greatest differentiation benefits.
2. Software maturity: high-performance math libraries, debugging tools, and CI/CD environments are still catching up to mature ecosystems. Critical projects need to reserve effort for adaptation and validation.
3. Hard constraints in high-performance scenarios: memory bandwidth, on-chip interconnect, and advanced process nodes are the determining factors for compute ceilings. Architectural advantages cannot replace system-level design capability.

Conclusion

From capital market recognition of RISC-V chip companies to continued investment in quantum computing, AI for Science, and brain-computer interfaces, it is clear that compute demand is expanding from cloud to edge and from general-purpose to specialized. For developers, RISC-V + AI offers a low-barrier, high-freedom technical stage. By understanding the vector extension programming model, following the engineering principle of prioritizing standard extensions while keeping custom instructions convergent, and applying a systematic performance methodology, you can build intelligent applications on an open architecture that deliver both performance and maintainability.