ConvMixer-768-32-ONNX

ConvMixer-768/32 (ONNX) – Renesas X5H

Introduction

This repository hosts ConvMixer-768/32, targeting the Renesas R-Car X5H platform for image classification inference on the NPX6 NPU.

• Model Architecture: ConvMixer — a "patches are all you need" architecture that applies a ViT-style patch embedding followed by repeated depthwise+pointwise convolution mixer blocks (no attention or MLP-mixer); this checkpoint uses hidden dimension 768 and depth 32
• Source Model: timm/convmixer_768_32.in1k — original checkpoint from the paper authors, locuslab/convmixer
• Task: Image Classification (ImageNet-1k, 1000 classes)
• Parameters: 21.1M (timm model card: Params (M): 21.1, GMACs: 19.5; paper reports ~21M params, 80.2% top-1 on ImageNet for this exact configuration)
• Paper: "Patches Are All You Need?" (Trockman & Kolter, 2022, arXiv:2201.09792)

Deployment Flow

The FP32 ONNX model is auto-cast to INT8 by the Renesas MWMX toolchain at compile time — no separate quantization step is required.

convmixer_768_32_..._optimized.onnx (FP32)
        │
        └─▶  MWMX Runtime  ──▶  INT8 auto-cast  ──▶  NPX6 NPU

Provided Artifacts

Performance

Measured on Renesas R-Car X5H via the MWMX runtime (APM50 ship-performance CI pipeline).

Benchmark configuration: Single NPU · Single AI Core · Batch size: 1 · Input: 3 × 224 × 224

This is among the higher-latency classifiers in this benchmark sweep, consistent with ConvMixer's relatively heavy patch-embedding + deep-mixer design (19.5 GMACs) compared to mobile-oriented CNNs of similar parameter count. Only the 1-AI-core slice was run in the source benchmark export — the 12-core slice was skipped, so no 12-core row is reported here.

Accuracy

TBD — not yet measured/published for this repo.

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Runtime Details

MWMX Runtime

• Engine: Renesas MWMX (Middleware MX) native inference runtime
• Input format: FP32 ONNX (compiled by the MWMX toolchain)
• NPU execution precision: INT8 (auto-cast by MWMX toolchain)
• Execution target: NPX6-48K NPU on R-Car X5H

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Prerequisites

To run inference on Renesas R-Car X5H, you need:

1. Renesas R-Car X5H board with NPX6 NPU
2. Renesas MWMX Runtime
3. Hugging Face CLI to download the model

Download

hf download Renesas/ConvMixer-768-32-ONNX --repo-type=model --include "fp32/*"

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Benchmark Methodology

• HIL runs: Hardware-in-the-loop — measured on physical R-Car X5H silicon via the MWMX runtime (metawaremx_runtime CI pipeline, "APM50" ship-performance target)
• Precision: FP32 ONNX input; INT8 execution (auto-cast by MWMX)
• Slices: only the 1 AI core slice was run for this model; the 12-core slice was skipped in the source export