Fergal Kilgannon ME Thesis Project 23-24
To use this framework, please cite:
F. Kilgannon, S. Kumar, V. Nguyen, T. Siriburanon, R. B. Staszewski, "Modelling, Data Mapping, and Evaluation of Analog Multiply-And-Accumulate Components in Quantized CNNs," IEEE ISSC, vol. 35, Jun. 2024, doi: 10.1109/ISSC61953.2024.10603107.
Found at: https://ieeexplore.ieee.org/document/10603107
Repository is split into "Software" and "Hardware". More information on each is available in READMEs conatined in the respective directories.
The software source contains code and notebooks to train and complete the forward-pass for three networks: a simple 6-layer MNIST CNN, simple 6-layer CIFAR-10 CNN, and extended 11-layer CIFAR-10 CNN. Note that when running locally, respective datasets will be downloaded to these directories, but not included in the GIT repo.
A third directory named "HardwareSpec" contains spreadsheets mapping the outputs for the 4x4 and 8x8 MAC results. Any further results should be placed in this folder, and referenced in the notebooks.
The hardware directory has a Hardware Mapping, Verilog, and PYNQ directory. The HardwareMapping directory contains a notebook and hardwareTests class to test different mapping techniques, and plot the results.
The Verilog and PYNQ directories have starter code for further work: The former with .V files describing the grid hardware, and the latter with the Python notebooks to control the CNN on a PYNQ board. This latter contains a notebook ForwardPass.ipynb which can run a single MNIST forward-pass using input from the user, and is good for use in monitoring data movement of the network.