This repository contains the code for both the PC side and the MCU side for my master thesis.

images: This folder contains some test images for the project.

json_files: This folder contains the recorded model files.

python_file: This folder contains the algorithms used to convert the original pytorch CNN models (in my case MobilenetV2 specifically) to JSON files, you can simply run hook.py to insert hooks in the model and then record the models in a JSON file. You can specify the number of layers you want to record in the line of if layer_id == ?. draw.py contains the code I used to produce some of my data image for my thesis paper.

src: This folder contains the Rust code I used to implement the project. In lib.rs, I implemented a simple yet effictive Rust representation of CNN models focusing on some basic functionalities like forward propagation and receptive field determination while making use of Rust's traits. In calculation.rs, I implemented some calculation functions which are only used for testing. In decode.rs, I implemented the function used to interpret the converted JSON files into Rust objects which are defined in lib.rs. In operations.rs, I implemented the algorithms used to distribute the models, as well as the method I used to do convolution using a flattened 1-D vector and weights data. In util.rs, I implemented some utility functions, the function name should show the functionality clearly.

main.rs: This is the main test and debugging bed for the distribution algorithm. The main function print the structure of the CNN model while the unit tests test the functionality of each individual function unit.

test_references: This folder contains the outputs produced by the original pytorch version. They are used as references in unit tests to judge the correctness of the algorithm used.

This folder contains the model file after model fusion.

This folder contains the code used to perform quantization on the converted JSON model.

python: This folder contains the code used to perform verification on the quantized model.

src: This folder contains the source code used to perform quantization on the model files. merge.rs contains the code used to perform layer fusion on the model file. quant.rs contains the code used to perform quantization after layer fusion, models are converted from 32bits to 8bits following https://arxiv.org/abs/1712.05877v1.

This folder contains the code used to perform simulation on a single PC.

src: distribution.rs contains the code used to generate the weights fragment data which is then stored in each individual "MCU" as a JSON file. node.rs contains the codes to simulate each worker node and coordinator node. simulation_setting.rs contains the setting for the simulation, including how each node communicate and work together. It is notable that when you are using a quantized model, you should use the function dubbed with quant instead of the original function. util.rs contains some auxiliary functions.

The folder contains the C++ arduino code for the actual implementation on a Teensy4.1 MCU board. You can ignore other folders except for download folder and worker_code folder.

download: This folder contains the code to download the weights fragment acquired through distribution.rs to MCU's FLASH memory using LittleFS.

worker_code: This folder contains the code used for performing one inference. You need to make sure that MCU are connected using ethernet through a network switch. calculation.h contains the code used to perform convolution on a 1-D vector. communication.h contains the code for collaboration of the MCUs.

This folder contains some crucial and helpful information after the distribution of the model weights and workload. The number in the file's name indicates the id of the MCU. input_length indicates the length of the current layer's input 1-D vector. result_length indicates the length of the output 1-D vector of the current layer. lines indicates for current layer's weights data,how many bytes should be read from FLASH memory. coor_lines indicates for current layer's coordinator data, how many bytes should be read from FLASH memory. All these 4 information can be acquired during the distribution of the models.

All of the MCU code has also been ported to platform IO, it is possible to open them as individual projects and flash them through VSCode with the PlatformIO extension. This has been done to allow the use of a single IDE for flashing. However, it is still necessary to open a new instance of VSCode to flash.

You can simply use cargo run --package Simulation to run simulation. In case that you want to test other CNN models (Not testd by me!), you need to follow the following steps.

1. Run hook.py to get the converted JSON file.

2. You can test the functionality of the model using unit tests in Algorithms\src\main.rs.

3. Run merge_batchnorm() in Quantization\merge.rs to perform layer fusion.

4. Run quantization on the fused model. Run quantize_layers_weights() to perform quantization on the weights, Run quantize_layers_activation() together with a calibration set to do quantization on the activations. record all the scales and zero points during the process. Later you can use the recorded scales and zero points of the activations together with scale and zero points of the weights to perform quantization using calculate_quantization().

5. Distribute the model and run the simulation using cargo run --package Simulation, remember to adjust the relevant parameters.

1. First run simulation to get the corresponding weights fragments. During the process, you can record the length of input and output of each layer in each MCU.

2. Connect the MCUs to a network switch, power up the MCUs.

3. Flash download.ino and filesys.h into the MCU, turn the MCU into download mode, run write_into_mcus.py on PC to download the weights fragments acquired in step 1 into the MCU. Repeat for all the MCUs. During the process, record the data bytes length sent back to PC.

4. Flash worker_code into each MCU, connect MCU to the PC, start the inference and monitor the process by running tcp.py on PC.