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deep learning hdl toolbox documentation -凯发k8网页登录

prototype and deploy deep learning networks on fpgas and socs

deep learning hdl toolbox™ provides functions and tools to prototype and implement deep learning networks on fpgas and socs. it provides pre-built bitstreams for running a variety of deep learning networks on supported xilinx® and intel® fpga and soc devices. profiling and estimation tools let you customize a deep learning network by exploring design, performance, and resource utilization tradeoffs.

deep learning hdl toolbox enables you to customize the hardware implementation of your deep learning network and generate portable, synthesizable verilog® and vhdl® code for deployment on any fpga (with hdl coder™ and simulink®).

get started

learn the basics of deep learning hdl toolbox

prototype deep learning networks on fpga

estimate performance of series networks. profile and retrieve inference results from target devices using matlab®

time series and sequence data networks

deploy networks trained for time series classification, regression, and forecasting tasks to target fpga and soc boards

deep learning processor customization and ip generation

configure, build, and generate custom bitstreams and processor ip cores, estimate and benchmark custom deep learning processor performance

system integration of deep learning processor ip core

generate the deep learning (dl) processor ip core by using hdl coder and deep learning hdl toolbox. integrate the generated deep learning (dl) processor ip core into your system design manually or by using hdl coder and ip core generation workflow

deep learning int8 quantization

calibrate, validate, and deploy quantized pretrained series deep learning networks

deep learning hdl toolbox supported hardware

support for third-party hardware such as intel and xilinx fpga boards

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