RISC-V vs ARM: Which Architecture is Best for AI Hardware?

Author: Srihari Maddula • Technical Lead, EurthTech

Reading Time: 25 mins

Topic: Silicon Architecture & AI Optimization

Bridging the gap between academic projects and industry reality.

Visualizing Ai processor chip glowing. Photo via Unsplash.

The explosion of artificial intelligence at the edge has created an insatiable demand for highly efficient, cost-effective, and scalable compute architectures. As we push more inferencing down to battery-powered sensors, smart appliances, and autonomous edge nodes, the silicon powering these devices has become the ultimate battleground.

For the past decade, ARM has been the undisputed king of embedded systems and mobile edge computing. But a formidable challenger has arrived, armed with an open-source ethos and a modular design philosophy: RISC-V.

In this deep dive, we'll explore the monumental shift happening in silicon architecture, comparing ARM's mature, proprietary ecosystem against the disruptive, open-standard approach of RISC-V. We'll dissect their distinct approaches to AI processing, evaluate code-level vector mathematics, and definitively answer the question: Which architecture is best for your next AI hardware project?

1. The Philosophy: Open-Source vs Licensed Architectures

The fundamental divergence between RISC-V and ARM lies not just in their instruction sets, but in their business models and underlying philosophies.

The ARM Paradigm: Licensed Reliability

ARM operates on a proprietary IP licensing model. Companies pay significant upfront fees and per-chip royalties to license either the Instruction Set Architecture (ISA) or pre-designed cores (like the Cortex-A or Cortex-M series). The advantage? Predictability, a massive existing software ecosystem, and verified designs that are guaranteed to work out-of-the-box. ARM provides a walled garden of excellence.

The RISC-V Rebellion: Open Innovation

RISC-V flips this script. It is an open-standard ISA provided under open-source licenses. Anyone can design, manufacture, and sell RISC-V chips without paying royalty fees. While you still have to design the core (or license a core design from vendors like SiFive or Andes), the lack of ISA licensing fees and the freedom to add custom instructions changes the economics of chip design entirely.

SENIOR SECRET

Custom Instruction Traps: While RISC-V allows you to define custom instructions to accelerate your specific AI models, be wary of the "ecosystem trap." If you rely heavily on custom, non-standard instructions, standard compilers (GCC/Clang) won't know how to use them without custom compiler intrinsic patches. You risk creating a silicon island that is incredibly difficult for standard software teams to target efficiently. Always baseline against standard RVV first.

Visualizing Abstract neural network circuit. Photo via Unsplash.

2. AI Processing Paradigms: NPU vs Vector

When it comes to executing AI workloads—which primarily consist of matrix multiplications and activation functions—ARM and RISC-V have taken distinctly different architectural paths for the edge.

ARM's Approach: Cortex-M + Ethos-U NPUs

ARM's primary strategy for high-performance edge AI relies on heterogeneous compute. They pair a standard microcontroller core (like a Cortex-M55 or Cortex-M85) with a dedicated Neural Processing Unit (NPU), such as the Ethos-U55 or Ethos-U65.

The CPU handles the control logic, standard operating system tasks, and data movement, while the NPU acts as an asynchronous accelerator for the heavy neural network operations.

SENIOR SECRET

Memory-Mapped NPU Bottlenecks: When pairing a Cortex core with an Ethos NPU, the biggest performance killer isn't compute—it's memory bandwidth. NPUs are incredibly fast but require data to be fed perfectly. If your NPU and CPU are fighting over the same internal SRAM bus, or if data isn't appropriately aligned in memory before triggering the NPU DMA, you'll lose all your acceleration to bus-stalls. Architect your memory hierarchy (TCM - Tightly Coupled Memory) specifically for the NPU's DMA patterns.

RISC-V's Approach: RVV 1.0 (Vector Extensions)

Instead of relying strictly on bolted-on NPUs, the RISC-V ecosystem heavily leverages the RVV (RISC-V Vector) 1.0 extension for in-core parallel processing.

RVV allows the CPU itself to process massive arrays of data simultaneously. It uses an elegant, scalable vector length (VLEN) approach. Code compiled for RVV can run on a processor with 128-bit vector registers just as easily as one with 512-bit registers, without recompilation.

SENIOR SECRET

SIMD vs Vector Tradeoffs: ARM's traditional NEON is a SIMD (Single Instruction, Multiple Data) architecture with fixed-width registers. RISC-V's RVV is a true Vector architecture. True vector architectures use a vector length register (`vl`) allowing strip-mining without tail-handling loops. This means RVV code is generally smaller and easier to write than traditional fixed-width SIMD code, reducing instruction cache misses during tight AI inference loops.

3. The Code: Vectorized MAC Comparison

At the heart of any neural network is the Multiply-Accumulate (MAC) operation. Let's look at how both architectures handle a vectorized MAC loop (e.g., computing a dot product) using C intrinsics.

ARM Cortex-M with Helium (M-Profile Vector Extension)

ARM's Helium (MVE) brings NEON-like capabilities down to the microcontroller level.

#include 
// ARM Helium Dot Product Snippet
float32_t arm_dot_product_helium(const float32_t pSrcA, const float32_t pSrcB, uint32_t blockSize) {
float32_t result = 0.0f;
uint32_t blkCnt = blockSize;
// Initialize an accumulator vector to zero
float32x4_t vecSum = vdupq_n_f32(0.0f);
while (blkCnt >= 4U) {
// Load 4 floats from A and B
float32x4_t vecA = vld1q_f32(pSrcA);
float32x4_t vecB = vld1q_f32(pSrcB);
// Fused Multiply-Accumulate
vecSum = vfmaq_f32(vecSum, vecA, vecB);
pSrcA += 4; pSrcB += 4; blkCnt -= 4U;
}
// Sum the elements of the accumulator vector (reduce)
result = vaddvq_f32(vecSum);
// Handle tail (omitted for brevity)
return result;
}

RISC-V with RVV 1.0

Notice how the vector length (`vl`) naturally handles the loop strip-mining and the tail elements automatically.

#include 
// RISC-V RVV 1.0 Dot Product Snippet
float rv_dot_product_v(const float pSrcA, const float pSrcB, size_t blockSize) {
size_t vl;
// Create scalar accumulator
vfloat32m1_t vecSum = __riscv_vfmv_v_f_f32m1(0.0f, __riscv_vsetvlmax_e32m1());
vfloat32m1_t vecZero = vecSum;
while (blockSize > 0) {
// Get dynamic vector length based on hardware capabilities and remaining blocks
vl = __riscv_vsetvl_e32m1(blockSize);
vfloat32m1_t vecA = __riscv_vle32_v_f32m1(pSrcA, vl);
vfloat32m1_t vecB = __riscv_vle32_v_f32m1(pSrcB, vl);
// Fused Multiply-Accumulate
vecSum = __riscv_vfmacc_vv_f32m1(vecSum, vecA, vecB, vl);
pSrcA += vl; pSrcB += vl; blockSize -= vl;
}
// Reduce sum
vfloat32m1_t result_vec = __riscv_vfredusum_vs_f32m1_f32m1(vecSum, vecZero, __riscv_vsetvlmax_e32m1());
return __riscv_vfmv_f_s_f32m1_f32(result_vec);
}

4. The 2026 Performance Landscape & Power Efficiency

As we look toward 2026, the performance-per-watt (PPW) metric is the holy grail for edge AI. Devices must process video streams or complex audio localizations while running on milliwatts.

ARM's tightly coupled Cortex-M + Ethos NPU combo currently leads in raw, out-of-the-box PPW for INT8 quantized neural networks. The hardware is highly optimized, and the silicon has gone through multiple generations of refinement.

However, RISC-V is closing the gap rapidly. Because the ISA is open, academic and commercial entities are stripping out unneeded CPU overhead, creating highly specialized, ultra-lean RVV implementations. When comparing purely on a compute-to-power ratio for specific workloads, custom RISC-V implementations are achieving near-parity with ARM.

SENIOR SECRET

Compiler-Aided Autovectorization: Writing assembly or intrinsics for every layer of your neural network is unscalable. The secret to edge AI performance lies in the compiler backend. LLVM's support for RISC-V autovectorization has matured immensely. By structuring your C/C++ loops cleanly and using `#pragma clang loop vectorize(enable)`, a modern Clang compiler targeting RVV 1.0 can often generate MAC loops that are 90% as efficient as hand-tuned assembly, saving months of engineering time.

5. Software Support: The CMSIS-NN vs Open-Source Battle

Hardware is just sand without software. This is where the battle lines are starkly drawn.

ARM's Moat: CMSIS-NN

ARM maintains the CMSIS-NN library, a heavily optimized software framework designed specifically to maximize neural network performance on Cortex-M cores and Ethos NPUs. It integrates seamlessly with TensorFlow Lite for Microcontrollers (TFLM). If you use ARM, getting a quantized model running efficiently takes days, not months. The tooling is enterprise-grade, fully supported, and highly predictable.

RISC-V's Strategy: Open-Source Vector Libraries

RISC-V relies on community-driven and vendor-specific libraries. Frameworks like IREE (Intermediate Representation Execution Environment) and Apache TVM are critical here. These MLIR-based compilers can ingest AI models and emit highly optimized RVV code. While the ecosystem is slightly more fragmented than CMSIS-NN, it is innovating at an incredible pace. SiFive and Andes also provide their own highly tuned NN libraries for their specific cores.

Visualizing Developer coding on multiple screens. Photo via Unsplash.

6. Cost & Ecosystem Realities

From an economic perspective, the numbers strongly favor the disruptor.

Developing a custom SoC utilizing RISC-V IP typically results in 30-60% lower licensing and royalty costs compared to utilizing an equivalent ARM Cortex + Ethos configuration. For high-volume edge devices (like smart home sensors or wearable health monitors), saving 50 cents on a $2 chip translates to massive margin improvements.

However, "free" ISA does not mean free development. The engineering hours required to integrate, verify, and write software for a custom RISC-V AI chip often offset the initial licensing savings. ARM's premium price tag pays for reduced time-to-market and lower risk.

7. Summary

The decision between RISC-V and ARM for AI hardware is no longer a simple technical comparison; it is a strategic business decision.

Choose ARM if:

• Time-to-market is your primary constraint.

• You need guaranteed, out-of-the-box performance via CMSIS-NN and Ethos NPUs.

• Your engineering team relies heavily on established, mature toolchains.

• You are building a general-purpose edge device where predictability is paramount.

Choose RISC-V if:

• You are optimizing for maximum volume and absolute minimum silicon cost (achieving 30-60% savings).

• Your AI workload benefits from custom instructions or highly specific vector lengths.

• You are willing to invest in compiler toolchains (LLVM/TVM) to unlock architectural flexibility.

• You want absolute control over your silicon destiny without proprietary licensing constraints.

As AI pushes further to the edge, the architecture that wins will be the one that provides the best balance of silicon flexibility and software friction. Right now, ARM holds the crown for usability, but RISC-V is rapidly democratizing the silicon, one vector instruction at a time.

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