Inference a Model in Small Microcontroller

 

                                            Photo by Google DeepMind

To improve model processing speed on a small microcontroller, you can consider the following strategies:

1. Optimize Your Model:

- Use a model that is optimized for edge devices. Some frameworks like TensorFlow and PyTorch

offer quantization techniques and smaller model architectures suitable for resource-constrained

devices.

- Prune your model to reduce its size by removing less important weights or neurons.

2. Accelerated Hardware:

- Utilize hardware accelerators if your Raspberry Pi has them. For example, Raspberry Pi 4

and later versions have a VideoCore VI GPU, which can be used for certain AI workloads.

- Consider using a Neural Compute Stick (NCS) or a Coral USB Accelerator, which can

significantly speed up inferencing for specific models.

3. Model Quantization:

- Convert your model to use quantized weights (e.g., TensorFlow Lite or PyTorch Quantization).

This can reduce memory and computation requirements.

4. Parallel Processing:

- Use multi-threading or multiprocessing to parallelize tasks. Raspberry Pi 4, for example, is a

quad-core device, and you can leverage all cores for concurrent tasks.

5. Use a More Powerful Raspberry Pi:

- If the model's speed is critical and you're using an older Raspberry Pi model, consider upgrading

to a more powerful one (e.g., Raspberry Pi 4).

6. Optimize Your Code:

- Ensure that your code is well-optimized. Inefficient code can slow down model processing. Use

profiling tools to identify bottlenecks and optimize accordingly.

7. Model Pruning:

- Implement model pruning to reduce the size of your model without significantly affecting its

performance. Tools like TensorFlow Model Optimization can help with this.

8. Implement Model Pipelining:

- Split your model into smaller parts and process them in a pipeline. This can improve throughput

and reduce latency.

9. Lower Input Resolution:

- Use lower input resolutions if acceptable for your application. Reducing the input size will speed

up inference but may reduce accuracy.

10. Hardware Cooling:

- Ensure that your Raspberry Pi has adequate cooling. Overheating can lead to thermal throttling

and reduced performance.

11. Distributed Processing:

- If you have multiple Raspberry Pi devices, you can distribute the processing load across them to

achieve higher throughput.

12. Optimize Dependencies:

- Use lightweight and optimized libraries where possible. Some deep learning frameworks have

optimized versions for edge devices.

13. Use Profiling Tools:

- Tools like `cProfile` and `line_profiler` can help you identify performance bottlenecks in your code.

Keep in mind that the level of improvement you can achieve depends on the specific model, hardware,

and application. It may require a combination of these strategies to achieve the

desired speedup.