Renesas/EfficientNet-Lite0-ONNX

EfficientNet-Lite0 (ONNX) – Renesas X5H

Introduction

This repository hosts EfficientNet-Lite0, targeting the Renesas R-Car X5H platform for image-classification inference on the NPX6 NPU.

• Model Architecture: EfficientNet-Lite — a mobile-optimized EfficientNet variant that replaces squeeze-and-excitation and swish activations with ReLU6 for better integer-quantization and fixed-point-hardware friendliness
• Source Model: timm/tf_efficientnet_lite0.in1k
• Task: image-classification (dataset: imagenet-1k)

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.

tf_efficientnet_lite0_in1k.onnx (FP32)
        │
        └─▶  MWMX Runtime  ──▶  INT8 auto-cast  ──▶  NPX6 NPU

Provided Artifacts

Artifact	Status	Notes
FP32 (ONNX)	⏳ Pending	fp32/tf_efficientnet_lite0_in1k.onnx — to be added; will be auto-cast to INT8 by the MWMX toolchain at compile time (see Deployment Flow above); no separate INT8 file will be shipped

Performance

Measured on Renesas R-Car X5H via the MWMX runtime (APM80 ship-performance CI pipeline).

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

AI Cores	Runtime	Precision	Device	Latency (ms)	Type
1	MWMX Runtime	INT8 (auto)	X5H · 1× NPU · 1 Core · 850 MHz	1.05	Measured
3	MWMX Runtime	INT8 (auto)	X5H · 1× NPU · 3 Core · 850 MHz	0.85	Measured
4	MWMX Runtime	INT8 (auto)	X5H · 1× NPU · 4 Core · 850 MHz	0.85	Measured
6	MWMX Runtime	INT8 (auto)	X5H · 1× NPU · 6 Core · 850 MHz	0.91	Measured
12	MWMX Runtime	INT8 (auto)	X5H · 1× NPU · 12 Core · 850 MHz	1.11	Measured

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/EfficientNet-Lite0-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, "APM80" ship-performance target)
• Precision: FP32 ONNX input; INT8 execution (auto-cast by MWMX)