Arm/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

Area	Result
Model format	ExecuTorch .pte
Target device class	Ethos-U NPU
Reference device	Alif DK-E8 (Cortex-M55, Ethos-U85 NPU, bare-metal)
Primary performance result	p50 latency 110.20 ms, 9.07 inferences per second
Accuracy result	Top-1 79.23%, Top-5 94.66%
Size / memory result	14.42 MB (6.76x smaller than the 97.45 MB FP32 baseline)

Original model

Field	Value
Original model	torchvision resnet50
Original source	torchvision/models/resnet.py
Original developer	Microsoft Research (architecture); weights from torchvision's modernized training recipe (IMAGENET1K_V2)
Original model card	torchvision.models.resnet50
Original license	BSD-3-Clause

Model files

File	Description
resnet50_ethosu_optimized_tensor.pte	Arm-optimized model for deployment
config.yaml	Model I/O contract used by the example
benchmarks/	FP32 baseline and Arm-optimized benchmark records

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

Field	Value
Device / platform	Alif DK-E8
CPU / accelerator	Cortex-M55 (1 core @ 400 MHz) + Ethos-U85 NPU (256 MACs/cycle @ 400 MHz)
OS	bare-metal
Runtime	ExecuTorch
Backend / delegate	Ethos-U (CMSIS-NN, Ethos-U-Driver)
Batch size	1
Precision	INT8
Runs	10 warmup runs, 100 measured runs

Performance results

Metric	Original / baseline	Arm-optimized	Improvement
p50 latency	N/A	110.20 ms	N/A
p90 latency	N/A	110.20 ms	N/A
Model size	97.45 MB	14.42 MB	6.76x smaller
Peak memory	N/A	3.28 MB estimated runtime SRAM peak (allocator pools only)	N/A

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.

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

Field	Value
Dataset	ImageNet-1k
Split	val
Number of samples	50000
Metric(s)	Top-1 accuracy, Top-5 accuracy
Evaluation runtime	PyTorch (FP32 vs. PT2E INT8)

Accuracy results

Metric	Original / baseline	Arm-optimized	Change
Top-1 accuracy	80.85%	79.23%	-1.63 pp
Top-5 accuracy	95.44%	94.66%	-0.78 pp

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:

Optimization area	Applied?	Notes
Model conversion	Yes	PT2E capture, then Arm Vela compiler compilation, then ExecuTorch .pte export with EthosUBackend delegation
Quantization	Yes	INT8 static post-training quantization, per-tensor granularity, weights symmetric and activations affine (asymmetric)
Runtime/backend selection	Yes	ExecuTorch with the EthosUBackend delegate; CMSIS-NN and Ethos-U-Driver optimisations
Graph/runtime compatibility updates	Yes	Performed as part of the Ethos-U85 export pipeline
Accuracy validation	Yes	Compared against the original model or published baseline
Performance validation	Yes	Measured on the reference Arm platform

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.

Expected input

Property	Value
Input shape	[1, 3, 224, 224]
Input type	float32
Input range	[0.0, 1.0]
Preprocessing	Resize to 232, center crop to 224x224, convert to tensor, normalize with ImageNet mean [0.485, 0.456, 0.406] and std [0.229, 0.224, 0.225]

Expected output

Property	Value
Output shape	[1, 1000]
Output type	Raw class logits (unnormalized)
Postprocessing	Softmax followed by top-5 selection

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.

Additional notes

• All layers were quantized; none were kept in FP32.
• Quantization detail: weights are symmetric per-tensor (zero-point fixed at 0); activations are asymmetric per-tensor affine (histogram-observer-derived zero-point) — described by Vela as an "a8w8" quantization scheme.
• The FP32 baseline was not exported/compiled to a .pte for Ethos-U85 (the Vela compiler requires a quantized graph), so no on-device latency, throughput, cycle, or memory figures exist for that profile; the corresponding table cells above read N/A.
• 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.

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.