resnet-50-int8-per-tensor-ethos-u-executorch

ResNet-50 optimized for Arm-based Ethos-U NPU

An INT8-quantized ResNet-50 image classification model, compiled with the Arm Vela compiler and exported to ExecuTorch's .pte format for the Arm Ethos-U85 NPU.

Summary

This repository contains an Arm-optimized version of torchvision's resnet50 (ResNet-50 architecture originally introduced by Microsoft Research) for image classification. The model is provided in ExecuTorch .pte format, targeting Ethos-U NPU systems.

This version is intended to demonstrate efficient inference on Arm-based platforms while preserving the original model's intended behavior. Arm has evaluated this model on ImageNet-1k and measured performance on a representative evaluation target.

Key results

Original model

Model files

Performance

Performance was measured on the reference configuration below. Results are intended to make the optimization reproducible but do not guarantee identical performance on every Arm-based system.

Reference configuration

Performance results

The "Original / baseline" model size is the FP32 PyTorch state dict.

Because the Ethos-U85 NPU architecture natively processes integer workloads and does not support floating-point execution, the FP32 baseline cannot be compiled into an Ethos-U85 delegate .pte file, making NPU-accelerated baseline latency and throughput figures non-applicable.

The Graviton environment documented for example.py verifies portability of the inference flow but does not provide or imply equivalent Graviton performance.

Accuracy

Accuracy was evaluated on the ImageNet-1k validation split, comparing the FP32 baseline against the Arm-optimized INT8 model via a PyTorch FP32-vs-PT2E INT8 simulation, not on the exported .pte running on Ethos-U85.

Evaluation setup

Accuracy results

A separate 2000-sample check on the Ethos-U85 Corstone-320 FVP emulator scored 79.25% Top-1 vs. 79.30% for the PyTorch-eager INT8 simulation on the same subsample (-0.05 pp), validating the PyTorch result as a reasonable proxy for on-device accuracy.

Accuracy was measured using the described evaluation setup. Users should re-evaluate the model on their own data before production use.

Arm optimization approach

Arm optimized this model for efficient inference on Arm-based platforms using a hardware-aware conversion and validation flow.

For this release, Arm used:

The goal of this process is to improve deployment characteristics such as latency, memory use, model size, and runtime compatibility while preserving the model's intended behavior. Detailed conversion scripts, calibration configuration, or backend-specific implementation details may be provided separately where appropriate.

Using this model

On an AWS Graviton (Linux arm64) host, run the standalone installer to build the Ethos-U85 execution stack that the example needs:

The installer uses a shared /opt/ethos-u85-graviton runtime when sudo is available, reusing existing builds across model bundles. Without sudo, it automatically falls back to a local runtime/ installation. A custom location can be set with ETHOS_RUNTIME_DIR, and existing installations are detected before rebuilding.

bash install_ethos_u85_graviton.sh

Dependencies are declared in pyproject.toml, which ships with this repository. Resolve and install them into a local virtual environment with uv:

uv python install
uv sync --frozen

Run the example

uv run example.py

The default run only prints predictions. To write a result without replacing the committed reference, pass an explicit destination:

uv run example.py --output /tmp/resnet50-predictions.json

The reference predictions.json records the known-good result for the committed sample_input.jpg. The example uses the FVP to validate the Ethos-U85 runtime path; it is not a Python-native inference path for this PTE.

Note: The Python/uv example runs on AWS Graviton (Ubuntu arm64) to confirm runtime compatibility only and is intended as a guideline for building an equivalent run script for Arm-based edge systems equipped with an Ethos-U85 NPU.

Expected input

Expected output

Intended use

This model is intended for developers evaluating image classification workloads on Arm-based platforms. It is suitable as a reference implementation for benchmarking, prototyping, and integration exploration.

Limitations

• Performance depends on the target device, runtime version, backend/delegate support, memory configuration, and system load.
• Accuracy was evaluated on ImageNet-1k val and may not generalize to all domains. The reported accuracy is based on a PyTorch quantized-vs-FP32 simulation pass, not a measurement of the exported .pte running on the Corstone-320 FVP or on Ethos-U85 silicon.
• This release preserves the original model's intended task and behavior, but users should validate it for their own application, data, and deployment environment.
• This repository is not a replacement for the original model documentation.
• Fixed input size of 224x224 — the shorter edge is resized to 232 with the aspect ratio preserved, then the center 224x224 region is cropped, so content outside that crop is discarded.
• Performance measurements are from the Alif DK-E8 development board; results may differ on other Arm devices.
• The exported .pte has nearly its entire compute graph (425 of 428 operators) delegated to a single Ethos-U85 NPU partition, with only 3 host ops running on the Cortex-M55 — it requires real Ethos-U85 hardware (an Alif DK-E8 board). On-device, the NPU sustains 99.97% effective cycle utilization, though theoretical MAC-slot utilization is at 36.27%.
• The example requires Linux arm64 for the included FVP installer.

Additional notes

• All layers were quantized; none were kept in FP32.
• This model targets bare-metal execution on the Alif DK-E8 board (Cortex-M55 host with Ethos-U85 NPU); actual on-device inference requires flashing the .pte to the board and running it through the ExecuTorch EthosUBackend delegate runtime, not a desktop Python run.
• Flash deployment configuration: the on-device benchmark used the 15,123,920-byte (45.07% of 32 MiB) Ethos-U85 .pte stored in the Alif DK-E8's OSPI1 flash.
• Sample input: sample_input.jpg is derived from Samoyed on Beach by Appleinfl, via Wikimedia Commons (public domain).

About this version

Original Model: torchvision resnet50 by Microsoft Research (architecture); weights from torchvision's modernized training recipe (IMAGENET1K_V2) - Repository

Optimization/conversion: Arm-Optimized version for execution on Arm-based platforms.

Converted/optimized by: Arm

License: The Original Model and the Optimized Model are subject to BSD-3-Clause.

This repository contains a converted or optimized version of the Original Model (the “Optimized Model”). The Original Model has been converted or optimized as described above for execution on Arm-based platforms.

No retraining or fine-tuning of the Original Model was performed as part of the conversion or optimization. The conversion or optimization was not intended to change the Original Model’s behavior or intended use.

Original Model and Documentation

For information about the Original Model, including its development, training data, intended uses, limitations and other relevant information, please refer to the Original Model repository. Information in that repository was provided by the original developer or other third parties and, unless expressly stated otherwise, has not been independently verified by Arm.

Licenses and Third-Party Terms

Use of the Original Model and the Optimized Model is subject to the applicable licenses, usage restrictions and other terms identified above and in the relevant repositories. Publication of the Optimized Model does not grant any rights beyond those provided under the applicable license terms.

You are responsible for reviewing those terms and ensuring that your use of the Original Model and the Optimized Model is permitted.

Purpose of this Release

The Optimized Model is provided as a reference implementation to demonstrate and evaluate execution and performance on Arm-based systems. It is not a production-ready or supported solution.

Arm’s publication of the Optimized Model does not constitute an endorsement or certification of the Original Model or a representation that the Optimized Model is suitable for production use or any particular purpose.

To the fullest extent permitted by applicable law (i) the Optimized Model is provided “as is.” Arm makes no representations or warranties that the Original Model, the Optimized Model or their outputs are accurate, safe, secure, non-infringing, legally compliant, suitable for production use or fit for any particular purpose; and (ii) Arm will not be liable for any loss or damage arising from or in connection with the Optimized Model, its use or its outputs.

You are responsible for independently evaluating the Optimized Model, its outputs and its suitability for your intended use, including compliance with applicable legal, regulatory, safety and security requirements.

Arm does not commit to provide ongoing support, maintenance or updates for the Optimized Model. Any use of or reliance on the Optimized Model or its outputs is at your own risk.