ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware
Paper
•
1812.00332
•
Published
ProxylessNAS
Use case : Image classification
Model description
ProxylessNAS enables direct neural architecture search on target hardware, eliminating the "proxy" task typically used in NAS. It learns specialized architectures optimized for specific devices without costly re-training.
The architecture employs direct hardware targeting by searching directly on target hardware metrics, using path-level binarization as an efficient search method with binary architecture parameters. Latency regularization incorporates actual latency into the search objective, resulting in hardware-specific architectures optimized for different hardware platforms.
ProxylessNAS achieves high accuracy (74.25% Top-1) with good quantization stability (0.60% drop), making it ideal for applications requiring hardware-optimized architectures with strict latency requirements.
(source: https://arxiv.org/abs/1812.00332)
The model is quantized to int8 using ONNX Runtime and exported for efficient deployment.
Network information
| Network Information | Value |
|---|---|
| Framework | Torch |
| MParams | ~4.13 M |
| Quantization | Int8 |
| Provenance | https://github.com/mit-han-lab/proxylessnas |
| Paper | https://arxiv.org/abs/1812.00332 |
Network inputs / outputs
For an image resolution of NxM and P classes
| Input Shape | Description |
|---|---|
| (1, N, M, 3) | Single NxM RGB image with UINT8 values between 0 and 255 |
| Output Shape | Description |
|---|---|
| (1, P) | Per-class confidence for P classes in FLOAT32 |
Recommended platforms
| Platform | Supported | Recommended |
|---|---|---|
| STM32L0 | [] | [] |
| STM32L4 | [] | [] |
| STM32U5 | [] | [] |
| STM32H7 | [] | [] |
| STM32MP1 | [] | [] |
| STM32MP2 | [] | [] |
| STM32N6 | [x] | [x] |
Performances
Metrics
- Measures are done with default STEdgeAI Core configuration with enabled input / output allocated option.
- All the models are trained from scratch on Imagenet dataset
Reference NPU memory footprint on Imagenet dataset (see Accuracy for details on dataset)
| Model | Dataset | Format | Resolution | Series | Internal RAM (KiB) | External RAM (KiB) | Weights Flash (KiB) | STEdgeAI Core version |
|---|---|---|---|---|---|---|---|---|
| proxylessnas_pt_224 | Imagenet | Int8 | 224×224×3 | STM32N6 | 1372 | 0 | 4233.20 | 3.0.0 |
Reference NPU inference time on food101 and imagenet dataset (see Accuracy for details on dataset)
| Model | Dataset | Format | Resolution | Board | Execution Engine | Inference time (ms) | Inf / sec | STEdgeAI Core version |
|---|---|---|---|---|---|---|---|---|
| proxylessnas_pt_224 | Imagenet | Int8 | 224×224×3 | STM32N6570-DK | NPU/MCU | 27.65 | 36.17 | 3.0.0 |
Accuracy with Imagenet dataset
| Model | Format | Resolution | Top 1 Accuracy |
|---|---|---|---|
| proxylessnas_pt | Float | 224x224x3 | 74.85 % |
| proxylessnas_pt | Int8 | 224x224x3 | 74.25 % |
| Model | Format | Resolution | Top 1 Accuracy |
|---|---|---|---|
| proxylessnas_pt | Float | 224x224x3 | 74.85 % |
| proxylessnas_pt | Int8 | 224x224x3 | 74.25 % |
Retraining and Integration in a simple example:
Please refer to the stm32ai-modelzoo-services GitHub here
References
[1] - Dataset: Imagenet (ILSVRC 2012) — https://www.image-net.org/
[2] - Model: ProxylessNAS — https://github.com/MIT-HAN-LAB/ProxylessNAS