Hands-on: Porting a Simple ML Model to Run on RISC-V (and Why NVLink Matters)

Hook: You're trying to ship models that actually run where customers need them — and the hardware landscape just got more complicated

Deploying a neural network to a tiny RISC-V device or a SiFive-based edge board shouldn't feel like a research paper. You want predictable latency, a small binary, and confidence your model will run on real silicon — not just in a cloud sandbox. At the same time, 2026 added a new twist: SiFive announced integration with NVIDIA's NVLink Fusion, opening fast, coherent links between RISC-V hosts and GPUs. That changes how we partition, optimize, and deploy models.

Why this project matters in 2026

Late 2025 and early 2026 saw two clear trends that affect every ML engineer and educator:

• RISC-V is maturing as a real deployment target — toolchains, RVV vector extensions, and production silicon (SiFive cores) are here.
• NVIDIA's NVLink Fusion (now being integrated into SiFive platforms) gives heterogeneous systems a high-bandwidth, low-latency interconnect — which means you can rethink which layers run on CPU vs GPU.

“SiFive will integrate NVIDIA's NVLink Fusion infrastructure with its RISC‑V processor IP platforms, allowing SiFive silicon to communicate with NVIDIA GPUs.” — Marco Chiappetta, Forbes (Jan 2026)

In short: the RISC-V device is no longer always the final execution site — it can be the control plane and pre/post-processor talking directly to a GPU over NVLink Fusion. But we still need compact, efficient model artifacts on the RISC-V side.

What you’ll build and learn — quick overview

• Convert a tiny CNN (MNIST-style) to ONNX and compile it for RISC‑V using TVM.
• Cross-compile a minimal runtime binary that runs on a riscv64 Linux environment (QEMU or SiFive board).
• Apply hardware-aware optimizations: RVV vectorization, int8 quantization, operator fusion, memory layout tuning.
• Design deployment strategies that leverage NVLink Fusion to offload heavy operators to an attached NVIDIA GPU.

Prerequisites — the short checklist

• Linux dev machine (Ubuntu 22.04+ recommended)
• Python 3.10+ with pip
• riscv64-linux-gnu cross-toolchain (GCC/LLVM) or Docker cross-build image
• QEMU for riscv64 or access to a SiFive board (U74-series) for testing
• TVM (v0.12+ in 2026) built with LLVM backend; ONNX and PyTorch for model conversion

Step 1 — A tiny model and ONNX conversion

Start small. Small models let you iterate fast and understand where the bottlenecks lie. Here’s a minimal PyTorch model (MNIST-style) you can convert to ONNX:

import torch
import torch.nn as nn

class TinyCNN(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv = nn.Conv2d(1, 8, 3, stride=1, padding=1)
        self.pool = nn.MaxPool2d(2)
        self.fc = nn.Linear(8*14*14, 10)
    def forward(self, x):
        x = self.pool(torch.relu(self.conv(x)))
        x = x.view(x.size(0), -1)
        return self.fc(x)

model = TinyCNN().eval()
dummy = torch.randn(1,1,28,28)
torch.onnx.export(model, dummy, 'tiny_cnn.onnx', opset_version=14)

Why ONNX?

ONNX gives a neutral IR you can ingest with TVM or TFLite. TVM gives more control for hardware-aware lowering and RVV optimizations — so we'll use TVM for the compilation path.

Step 2 — Set up TVM for RISC‑V

Build TVM with LLVM and configure a riscv64 target. In 2026, TVM includes improved support for RVV (RISC‑V Vector extension). Use an LLVM toolchain that targets riscv64; many distros provide cross-LLVM builds, or you can build clang/LLVM with riscv targets.

# Example minimal target string in TVM
import tvm
from tvm import relay

target = tvm.target.Target(
    "llvm -mtriple=riscv64-unknown-linux-gnu -mcpu=sifive-u74 -mattr=+v,+zmmul"
)
# If you have RVV variant, include +v; tune -mcpu to match the board

Key target knobs:

• -mcpu: tune to the SiFive core variant you target (u54/u74 etc.)
• -mattr: enable RVV (+v), floating-point extensions (+f,+d), or any vendor SIMD features

Step 3 — Relay flow: load ONNX, optimize, tune

Workflow for TVM (simplified):

1. Load ONNX into Relay.
2. Run graph-level optimizations and operator fusion (Relay passes).
3. Apply auto-scheduler or AutoTVM for per-op tuning (important for RVV).
4. Build the module for the riscv64 target, produce a shared object or static binary.

import onnx
from tvm import relay
onnx_model = onnx.load('tiny_cnn.onnx')
mod, params = relay.frontend.from_onnx(onnx_model, shape_dict={'input': (1,1,28,28)})

# Graph-level optimizations
mod = relay.transform.InferType()(mod)

with tvm.transform.PassContext(opt_level=3):
    lib = relay.build(mod, target=target, params=params)
    lib.export_library('tvm_riscv_tiny.so')

Note: this is the minimal path. For real speedups, use the TVM auto-scheduler to tune convolution kernels for RVV and your exact -mcpu.

Step 4 — Cross-compile a small runtime

You need a runtime binary that loads the compiled module and runs inference on the target. TVM's C runtime (micro or full) can be cross-compiled. Example cross-compile steps:

export CROSS_COMPILE=riscv64-linux-gnu-
# Build a minimal C++ runner that links tvm_runtime and your shared lib
riscv64-linux-gnu-g++ -static -O3 runner.cpp tvm_riscv_tiny.so -o tiny_runner

Test locally with QEMU:

qemu-riscv64 -L /usr/riscv64-linux-gnu/ ./tiny_runner

If you see correct output, congratulations — you have an end-to-end pipeline from PyTorch -> ONNX -> TVM -> RISC‑V binary.

Optimization checklist — squeeze the last milliseconds

Use this checklist iteratively. Measure after each step.

• RVV vectorization: Ensure TVM emits RVV code. Confirm with the generated LLVM IR or assembler contains vector instructions. If not, include -mattr=+v and tune schedule to utilize vector lanes.
• Quantization: Apply post-training quantization to int8 using TVM or TFLite. Int8 reduces memory and speeds up on integer-friendly cores. Consider QAT if accuracy loss is unacceptable.
• Operator fusion: Use Relay fusion passes to reduce memory traffic between ops.
• Memory layout: Use NCHW vs NHWC depending on vector lanes. For RVV, packing channels for vector-width alignment yields benefits.
• Reduce dynamic allocs: Prefer static workspace allocations; pre-allocate large buffers for intermediate tensors.
• Threading: If the SoC core supports multiple HW threads, use OpenMP or TVM’s thread pool cautiously — synchronisation costs can dominate.

Profiling methodology

Measure properly on target (or an accurate QEMU profile). Time end-to-end, but also per-op:

• Collect wall-clock latency (median and p95).
• Use cycle counters if available on silicon (CSR performance counters) for micro-benchmarks.
• Measure memory bandwidth usage — this often limits small-core performance.

Where NVLink Fusion changes the game

Before NVLink Fusion was widely available, the deployment decision was often binary: either ship the whole model to an edge CPU, or offload everything to the cloud or an attached GPU over a PCIe/aligned NIC. With NVLink Fusion integrated into SiFive platforms, the interconnect is fast and coherent, and that unlocks a third, very pragmatic option:

• Hybrid execution: Keep low-latency control, preprocessing, and tiny models on the RISC-V host; offload compute-heavy layers or large attention blocks to the GPU over NVLink Fusion.
• Zero-copy data sharing: NVLink Fusion enables coherent memory mappings and fast DMA — reducing serialization and copy overhead between CPU and GPU address spaces.
• Dynamic partitioning and streaming: Models can be partitioned at runtime based on current load, temperature, or power constraints. A RISC‑V host can stream activations to the GPU for heavy layers then read results back with far less overhead than traditional PCIe paths.

Practical deployment patterns

1. Layer-wise offload: run early convs on RISC‑V (for early pruning and compression), send mid-to-late heavy layers to the GPU.
2. Operator-level offload: run embedding lookup or small convolution on CPU, matrix multiply-heavy ops on GPU.
3. Pipeline parallelism: while GPU processes batch N, RISC-V prepares batch N+1 using NVLink FIFO semantics.

How to implement offload in practice (patterns)

Here are pragmatic integration methods you can use today:

• RPC via TVM’s remote runtime: Expose the GPU as a remote device; TVM supports RPC-style remote modules. With NVLink Fusion, this RPC becomes lower latency and higher throughput.
• Shared memory + CUDA-aware runtime: If NVIDIA provides CUDA drivers for RISC‑V + NVLink Fusion, you can use CUDA IPC or CUDA Unified Memory semantics to avoid copies (watch driver support timeline in 2026).
• Split binary approach: Build a small controller on RISC‑V that sends tensor descriptors and pointers to GPU-resident code. The GPU binary executes kernels and writes back to NVLink-mapped memory.

Design considerations and trade-offs

NVLink Fusion gives you bandwidth, but it doesn't eliminate design trade-offs:

• Latency vs Throughput: For ultra-low-latency (sub-ms) inference on tiny inputs, staying entirely on RISC‑V is often best. For throughput or heavy models, offloading to GPU via NVLink Fusion shines.
• Power and cost: SiFive + GPU setups increase BOM and power. Decide based on per-inference energy and service-level targets.
• Reliability and isolation: Tight coupling via NVLink Fusion requires careful error handling, watchdogs, and security boundaries — especially for remote or safety-critical systems.

Example hybrid pipeline (conceptual)

1. Input arrives at RISC-V edge host.
2. RISC‑V performs preprocessing and a small quantized conv block (fast, local).
3. Activations are placed in an NVLink-shared buffer.
4. GPU runs the heavy backbone layers (batched) and writes results back to the shared buffer.
5. RISC-V fetches predictions, runs final post-processing, and handles outputs/actuation.

This pipeline reduces end-to-end latency compared to sending data over the network and keeps local control while leveraging GPU compute.

Security & software-stack notes (2026)

By 2026, several vendor stacks began providing RISC‑V drivers and NVLink-aware runtime libraries. When you build a hybrid system:

• Validate driver versions and ABI compatibility — mismatches upstream can break unified memory semantics.
• Harden RPC endpoints and ensure secure DMA memory pools to avoid unauthorized memory reads over the NVLink domain.
• Prefer signed kernel modules and verified boot for deployed SiFive boards with GPU attachments.

Benchmark plan — what to measure

Measure these axes for informed decisions:

• Per-inference latency (median, p95)
• Batch throughput (inferences/sec)
• CPU utilization on RISC-V and GPU utilization
• Memory traffic across NVLink (if available) and overhead of copies
• Energy per inference

Realistic expectations

RISC‑V cores are catching up but won’t beat modern data-center GPUs on heavy matrix math. The real win is locality and flexible partitioning: keep control and latency-sensitive routines on RISC‑V, and use NVLink Fusion to make the GPU feel like an extension of the host memory space. That hybrid model is especially powerful in 2026, when SiFive+NVLink Fusion platforms are emerging.

Common pitfalls and how to avoid them

• Assuming perfect unified memory: Even with NVLink Fusion, driver compatibility and cache-coherence corner cases exist. Always validate with microbenchmarks.
• Over-threading on small CPUs: Too many threads on a small RISC‑V core kill performance. Start single-threaded and scale up carefully.
• Missing vectorization: Ensure toolchains and TVM know the CPU supports RVV. If your generated code lacks vector ops, adjust -mattr and auto-scheduler configs.

Advanced strategies (for production)

• Auto-partitioning based on profiles: Use per-layer profiling to decide at deployment whether to offload a layer to GPU or keep it local.
• Adaptive precision: Dynamically lower precision (float16/int8) on GPU when throughput is key, or increase precision when accuracy drift is detected.
• Model morphing: Ship multiple subgraphs tuned for local execution and GPU execution, and pick at runtime based on latency/power constraints.

Final checklist — get to a working prototype quickly

1. Build the tiny ONNX model and validate in PyTorch.
2. Install TVM with LLVM + riscv targets; set -mattr for RVV if available.
3. Compile with TVM, tune the conv/matmul kernels for your -mcpu.
4. Cross-compile the runtime and test in QEMU or on a SiFive board.
5. Profile. Apply quantization and memory layout changes. Re-profile.
6. If you have NVLink Fusion hardware, implement a simple offload for one heavy operator and measure copy vs NVLink zero-copy performance.

Where to go next — resources and next projects

Next steps to deepen this skillset:

• Try a larger model and experiment with layer partitioning heuristics.
• Implement auto-scaling between local and GPU execution based on power/thermals.
• Contribute kernel schedules for RVV back into TVM and share performance results.

Closing: why this is a high-impact skill for 2026

Knowing how to compile and optimize models for RISC‑V — and how to partition workloads with NVLink Fusion-enabled GPUs — makes you valuable in two ways: you can deliver small, reliable inference artifacts on edge silicon, and you can design systems that escalate to GPU compute without costly data moves. Employers in 2026 are hunting engineers who can navigate heterogeneous stacks and squeeze production-grade inference out of tight hardware. This project gives you a hands-on path to do exactly that.

Call to action

Ready to build this end-to-end prototype? Clone a starter repo (TinyCNN -> ONNX -> TVM -> RISC‑V runner), follow the scripts, and run the benchmarks on QEMU or a SiFive board. Share your performance numbers, RVV assembly snippets, or NVLink Fusion offload designs in the comments or on your GitHub — and if you want, I’ll review and suggest optimizations. Start small, measure every change, and iterate toward a hybrid deployment that fits your latency, cost, and power goals.

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