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

Deep Learning HDL Toolbox™ provides functions to configure, build, and generate custom bitstreams and a custom processor IP. Obtain performance and resource utilization of a pretrained series network on the custom processor. Optimize the custom processor by using the estimation results.

Classes

dlhdl.ProcessorConfig Configure custom deep learning processor

Functions

`dlhdl.buildProcessor`	Build and generate custom processor IP
`estimatePerformance`	Retrieve layer-level latencies and performance by using `estimatePerformance` method
`estimateResources`	Return estimated resources used by custom bitstream configuration
`getModuleProperty`	Use the `getModuleProperty` method to get values of module properties within the `dlhdl.ProcessorConfig` object
`setModuleProperty`	Use the `setModuleProperty` method to set properties of modules within the `dlhdl.ProcessorConfig` object

Topics

Custom Processor Configuration

Custom Processor Configuration Workflow

Accelerate the estimation and optimization of custom deep learning processor by configuring parameters of the conv processor and fc processor, created by using the dlhdl.ProcessorConfig object workflow.

Estimate Performance of Deep Learning Network

Analyze the deep learning network layer level latencies and overall performance before deployment.

Estimate Resource Utilization for Custom Processor Configuration

Expedite the time to identify a target hardware board that meets resource utilization budgets before deployment.

Effects of Custom Deep Learning Processor Parameters on Performance and Resource Utilization

Rapidly prototype custom processor configuration and networks by understanding how deep learning processor parameters affect resource utilization and network performance.

Custom Processor Code Generation

Generate Custom Bitstream

Rapidly prototype and iterate custom deep learning networks performance by configuring, building and generating custom bitstreams which can then be deployed to target FPGA and SoC boards.

Generate Custom Processor IP

Build and generate IP for the dlhdl.ProcessorConfig

Featured Examples

Custom Deep Learning Processor Generation to Meet Performance Requirements

Create a custom processor configuration and estimate the performance of a pretrained series network. You can then modify parameters of the custom processor configuration and re-estimate the performance. Once you have achieved your performance requirements you can generate a custom bitstream by using the custom processor configuration.

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Customize Bitstream Configuration to Meet Resource Use Requirements

The user wants to deploy a digit recognition network with a target performance of 500 frames per second (FPS) to a Xilinx ZCU102 ZU4CG device. The target device resource counts are:

Open Live Script

Deep Learning HDL Toolbox Documentation

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