hls4ml.backends.vivado_accelerator package

Subpackages

Submodules

hls4ml.backends.vivado_accelerator.vivado_accelerator_backend module

class hls4ml.backends.vivado_accelerator.vivado_accelerator_backend.VivadoAcceleratorBackend

Bases: VivadoBackend

build(model, reset=False, csim=True, synth=True, cosim=False, validation=False, export=False, vsynth=False, fifo_opt=False, bitfile=False)
create_initial_config(board='pynq-z2', part=None, clock_period=5, clock_uncertainty='12.5%', io_type='io_parallel', interface='axi_stream', driver='python', input_type='float', output_type='float', platform='xilinx_u250_xdma_201830_2')

Create initial accelerator config with default parameters

Parameters:

  • board – one of the keys defined in supported_boards.json

  • clock_period – clock period passed to hls project

  • io_type – io_parallel or io_stream

  • interface – axi_stream: generate hardware designs and drivers which exploit axi stream channels. axi_master: generate hardware designs and drivers which exploit axi master channels. axi_lite : generate hardware designs and drivers which exploit axi lite channels. (Don’t use it to exchange large amount of data)

  • driver – python: generates the python driver to use the accelerator in the PYNQ stack. c: generates the c driver to use the accelerator bare-metal.

  • input_type – the wrapper input precision. Can be float or an ap_type. Note: VivadoAcceleratorBackend will round the number of bits used to the next power-of-2 value.

  • output_type – the wrapper output precision. Can be float or an ap_type. Note: VivadoAcceleratorBackend will round the number of bits used to the next power-of-2 value.

  • platform – development target platform

Returns:

populated config

get_default_flow()

The name of the default flow of the backend.

Default flow is used as the conversion target if the target flow has not been specified.

get_writer_flow()
make_xclbin(model, platform='xilinx_u250_xdma_201830_2')

Create the xclbin for the given model and target platform.

Parameters:

  • model (ModelGraph) – Compiled and build model.

  • platform (str, optional) – Development/Deployment target platform, must be installed first. The host machine only requires the deployment target platform. Refer to the Getting Started section of the Alveo guide. Defaults to ‘xilinx_u250_xdma_201830_2’.

hls4ml.backends.vivado_accelerator.vivado_accelerator_config module

class hls4ml.backends.vivado_accelerator.vivado_accelerator_config.VivadoAcceleratorConfig(config, model_inputs, model_outputs)

Bases: object

get_board()
get_board_info(board=None)
get_clock_period()
get_corrected_types()
get_driver()
get_driver_file()
get_driver_path()
get_input_type()
get_interface()
get_io_bitwidth()
get_krnl_rtl_src_dir()
get_output_type()
get_part()
get_platform()
get_tcl_file_path()

Module contents