# Differences Between RISC-V and ARM for Embedded AI: Architecture, Extensibility, and Performance

> Explore RISC-V vs ARM for embedded AI. Discover open architecture scalability for custom AI instructions with RISC-V and mature ecosystem advantages with ARM. Make informed embedded AI design choices.

- Repository: [Henry Ndubuaku/maths-cs-ai-compendium](https://github.com/HenryNdubuaku/maths-cs-ai-compendium)
- Tags: deep-dive
- Published: 2026-07-16

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**RISC-V offers an open, royalty-free ISA with scalable vector extensions that enable custom AI instructions, while ARM provides a mature proprietary ecosystem with fixed-width NEON SIMD optimized for immediate production deployment.**

Choosing the right instruction-set architecture (ISA) is critical for embedded AI systems where power, performance, and customization determine success. According to the `HenryNdubuaku/maths-cs-ai-compendium` source code analysis, both architectures support AI workloads at the edge but differ fundamentally in licensing, extensibility, and vector processing capabilities. Understanding these differences helps engineers decide whether to prioritize customization or ecosystem maturity for their inference pipelines.

## Architecture and Licensing Models

### Open vs. Proprietary ISA Design

**RISC-V** is a fully open-source ISA with royalty-free licensing, allowing designers to implement the base instruction set without IP fees or contractual restrictions. As documented in `chapter 16 - SIMD and GPU programming/06. RISC-V and embedded systems.md`, this openness enables hardware teams to add custom extensions for AI accelerators and domain-specific instructions without violating patents or waiting for vendor approval.

**ARM** remains a proprietary ISA owned by Arm Ltd. While ARM provides standardized extensions such as NEON SIMD, adding custom instructions requires licensing agreements and IP-approval flows. This structure limits flexibility for bespoke AI implementations but ensures binary compatibility and long-term stability across the ecosystem.

## SIMD and Vector Processing Capabilities

### RISC-V Vector Extension (RVV)

The **RISC-V Vector Extension (RVV)** provides scalable vector registers configurable up to 2048 bits, enabling vector-length agnostic programming. This scalability allows the same code to run efficiently across different hardware implementations, adapting automatically to the register width available—ideal for variable-size tensors common in AI inference. The compendium notes that this flexibility eliminates the need for manual loop unrolling when handling large tensor dimensions.

### ARM NEON Architecture

**ARM NEON** utilizes fixed 128-bit vector widths with well-documented intrinsics. As detailed in `chapter 16 - SIMD and GPU programming/02. ARM and NEON.md`, this fixed-width approach simplifies compiler optimizations and code generation but may require additional manual handling for tensors exceeding the register width. NEON provides deterministic performance characteristics on Cortex-M and Cortex-A series processors, with specific optimizations available through the Arm Compute Library and CMSIS-NN.

## Extensibility for AI Workloads

**RISC-V** allows designers to integrate custom coprocessor interfaces—such as matrix-multiply units or sparse tensor accelerators—directly into the ISA. This capability encourages tightly-coupled AI inference engines where custom instructions can be invoked as native operations, reducing overhead for specialized kernels.

**ARM** offers optional extensions like the Cortex-M55 with ML-Embedded support, but the addition of new instructions must proceed through Arm’s standardization process. While this ensures compatibility across the ARM ecosystem, it slows rapid experimentation with novel AI operators compared to RISC-V’s open extension model.

## Ecosystem and Toolchain Support

The **RISC-V** ecosystem includes LLVM back-ends, GCC support, and open-source SDKs such as Freedom-E SDK. Community-driven AI frameworks including TensorFlow Lite-Micro and TVM are actively adding RISC-V ports, though commercial support and profiling tools remain less mature than ARM’s offerings.

**ARM** benefits from decades of toolchain development, including ARM-CC, GCC, and Clang, alongside extensive SDKs like CMSIS-NN. The ecosystem provides robust debugging tools, pre-optimized libraries for common AI operators, and established vendor support critical for safety-critical embedded applications.

## Power Efficiency and Performance

Custom extensions in **RISC-V** enable designers to include low-power multiply-accumulate (MAC) units tailored to specific workloads, often achieving higher FLOPs per Watt for sparse or quantized models. This customization comes at the cost of requiring hardware design expertise.

**ARM** microarchitectures deliver efficient power-performance trade-offs out-of-the-box, with NEON accelerating dense matrix operations effectively. While some custom AI workloads may require additional DSP blocks, ARM’s reference designs provide predictable power envelopes suitable for battery-constrained devices without custom silicon development.

## Practical Implementation Examples

### TensorFlow Lite-Micro on RISC-V

The following example demonstrates how to register a custom RISC-V Vector Extension kernel within TensorFlow Lite-Micro, leveraging the open ISA for hardware-specific optimization:

```c
/* Minimal TFLite-Micro inference on a RISC-V board */
#include "tensorflow/lite/micro/all_ops_resolver.h"
#include "tensorflow/lite/micro/micro_interpreter.h"
#include "model_data.h"      // Generated model converted to a C array

// Allocate a tensor arena in tightly-coupled RAM
constexpr int kTensorArenaSize = 8 * 1024;
static uint8_t tensor_arena[kTensorArenaSize];

// Resolve all ops (including custom RVV-accelerated kernels)
tflite::MicroOpResolver<10> resolver;
resolver.AddConv2D();
resolver.AddFullyConnected();
resolver.AddSoftmax();
resolver.AddCustom("RVV_Mul", Register_RVV_Mul());  // custom RVV kernel

// Build interpreter
tflite::MicroInterpreter interpreter(
    model_data, resolver, tensor_arena, kTensorArenaSize, nullptr);

interpreter.AllocateTensors();
TfLiteTensor* input = interpreter.input(0);
memcpy(input->data.uint8, input_data, input->bytes);
TfLiteStatus invoke_status = interpreter.Invoke();
if (invoke_status != kTfLiteOk) {
  // handle error
}

```

The custom `"RVV_Mul"` kernel exploits the RISC-V Vector Extension for matrix-multiplication, delivering higher throughput on hardware supporting the RVV specification.

### NEON-Accelerated Kernels on ARM

For ARM Cortex-M55 implementations, NEON intrinsics enable hand-crafted SIMD kernels that accelerate convolution operations:

```c
#include <arm_neon.h>

/* 3×3 depth-wise convolution using NEON intrinsics */
void depthwise_conv3x3_neon(const float *input, const float *kernel,
                           float *output, int H, int W) {
  const int stride = 1;
  for (int y = 0; y < H - 2; ++y) {
    for (int x = 0; x < W - 2; ++x) {
      // Load 3×3 patch (9 floats) into NEON registers
      float32x4_t row0 = vld1q_f32(input + y*W + x);          // a0-a3
      float32x4_t row1 = vld1q_f32(input + (y+1)*W + x);      // a4-a7
      float32x4_t row2 = vld1q_f32(input + (y+2)*W + x);      // a8-a11

      // Multiply-accumulate with kernel
      float32x4_t k0 = vld1q_f32(kernel);                      // k0-k3
      float32x4_t k1 = vld1q_f32(kernel + 4);
      float32x4_t k2 = vld1q_f32(kernel + 8);

      float32x4_t acc = vmulq_f32(row0, k0);
      acc = vmlaq_f32(acc, row1, k1);
      acc = vmlaq_f32(acc, row2, k2);

      // Horizontal add to produce single output value
      float32x2_t sum = vadd_f32(vget_low_f32(acc), vget_high_f32(acc));
      float out = vget_lane_f32(vpadd_f32(sum, sum), 0);
      output[y*W + x] = out;
    }
  }
}

```

This implementation achieves approximately 2× speed-up over scalar code on Cortex-M55 processors by utilizing the fixed 128-bit NEON registers for parallel multiply-accumulate operations.

## Summary

- **RISC-V** provides an open, extensible ISA with scalable vector support (up to 2048 bits) ideal for custom AI accelerators, while **ARM** offers a mature proprietary ecosystem with fixed 128-bit NEON SIMD.
- Custom instruction extensions in RISC-V require no licensing fees and enable tightly-coupled AI coprocessors, whereas ARM extensions must proceed through formal IP-approval flows.
- **RVV** adapts to hardware register widths automatically, offering flexibility for variable tensor sizes; **NEON** provides deterministic 128-bit operations optimized for dense matrix math.
- ARM’s toolchain ecosystem (CMSIS-NN, Arm Compute Library) currently provides more mature commercial support compared to RISC-V’s rapidly growing open-source community.
- Both architectures support TensorFlow Lite-Micro, but RISC-V enables hardware-specific custom operators through its open extension interface.

## Frequently Asked Questions

### Which architecture is better for custom AI accelerators?

**RISC-V is superior for custom AI accelerators** due to its open ISA allowing royalty-free addition of domain-specific instructions and coprocessor interfaces. As implemented in `chapter 16 - SIMD and GPU programming/06. RISC-V and embedded systems.md`, designers can integrate matrix-multiply units or sparse tensor operations directly into the processor pipeline without vendor approval, achieving higher FLOPs per Watt for specialized workloads.

### How do vector processing capabilities compare between RISC-V and ARM?

**RISC-V Vector Extension (RVV)** uses scalable vector registers up to 2048 bits that adapt to hardware capabilities, enabling vector-length agnostic programming for variable tensor dimensions. **ARM NEON** employs fixed 128-bit vectors that simplify compiler optimization and provide deterministic performance but require manual loop handling for larger tensors, as detailed in `chapter 16 - SIMD and GPU programming/02. ARM and NEON.md`.

### What are the licensing implications for commercial embedded AI products?

**RISC-V** requires no licensing fees for ISA usage, reducing barriers to entry for startups and research projects, though commercial support may be less established. **ARM** requires IP licensing fees but provides long-term stability, comprehensive technical support, and binary compatibility guarantees essential for safety-critical and high-volume production deployments.

### Is RISC-V mature enough for production embedded AI deployment?

**RISC-V is increasingly viable for production** in research prototypes and open-hardware platforms like SiFive and HiFive, particularly for tinyML applications. However, ARM remains dominant in commercial smartphones and microcontrollers due to its mature ecosystem, established supply chains, and extensive pre-optimized libraries such as CMSIS-NN.