da4ml: Distributed Arithmetic for Machine Learning

This project performs Constant Matrix-Vector Multiplication (CMVM) with Distributed Arithmetic (DA) for Machine Learning (ML) on a Field Programmable Gate Arrays (FPGAs).

CMVM optimization is done through greedy CSE of two-term subexpressions, with possible Delay Constraints (DC). The optimization is done in jitted Python (Numba), and a list of optimized operations is generated as traced Python code.

At the moment, the project only generates Vitis HLS C++ code for the FPGA implementation of the optimized CMVM kernel. HDL code generation is planned for the future. Currently, the major use of this repository is through the distributed_arithmetic strategy in the hls4ml project.

Installation

The project is available on PyPI and can be installed with pip:

pip install da4ml

Notice that numba>=6.0.0 is required for the project to work. The project does not work with python<3.10. If the project fails to compile, try upgrading numba and llvmlite to the latest versions.

hls4ml

The major use of this project is through the distributed_arithmetic strategy in the hls4ml:

model_hls = hls4ml.converters.convert_from_keras_model(
    model,
    hls_config={
        'Model': {
            ...
            'Strategy': 'distributed_arithmetic',
        },
        ...
    },
    ...
)

Currently, Dense/Conv1D/Conv2D layers are supported for both io_parallel and io_stream dataflows. However, notice that distributed arithmetic implies reuse_factor=1, as the whole kernel is implemented in combinational logic.

Notice

Currently, only the da4ml-v3 branch of hls4ml supports the distributed_arithmetic strategy. The da4ml-v3 branch is not yet merged into the main branch of hls4ml, so you need to install it from the GitHub repository.