A floating-point matrix multiplication implemented in hardware.

This repo describes the implementation of a floating-point matrix multiplication on a PYNQ-Z1 development board.

The hardware module implements the matrix product C = AB, where A, B, and C are 128 x 128 matrices.

This hardware accelerator provides a 2.8x speedup compared to NumPy. It should be noted that NumPy uses both vectorization and, presumably, a more efficient algorithm than the naive one implemented in this example.

A 3.5x speedup can be achieved by using the 64-bit AXI-Stream interface. This approach requires additional logic to pack and unpack the matrices.

• [hls] contains the accelerator c++ source code for high level synthesis.
• [boards/Pynq-Z1/matmult] contains the Vivado project.
• [notebooks] contains the Jupyter Notebook to evaluate the design. This notebook uses the Xilinx/PYNQ Python library.
• [overlay] contains the generated hardware files. These files were generated using vivado and vivado_hls version 2019.2.

• Copy overlay/matmult to the PYNQ-Z1 device.
• Copy notebooks/matmult.ipynb to the Jupyter notebooks area in the PYNQ-Z1 device.

Requires Xilinx vivado and vivado_hls version 2019.2. If necessary, a different version can be configured in the tcl scripts: script_solution1.tcl and matmult.tcl.

• Build the matmult module:

  cd hls
  make clean && make solution1

• Build the Vivado project:

  cd boards/Pynq-Z1/matmult
  make clean  && make all

• This implementation borrows ideas and code from this application note, and the PYNQ hello world example.
• Schematic of matrix multiplication taken from Wikipedia