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
`optimizeConfigurationForNetwork`	Retrieve optimized network-specific deep learning processor configuration
`openCustomLayerModel`	Open a generated custom layer verification model to verify your custom layers
`registerCustomLayer`	Register the custom layer definition and万博1manbetxmodel representation of the custom layer
`verifyCustomLayerModel`	Verify the functionality and accuracy of the custom layer by using the generated custom layer verification model

Topics

Custom Processor Configuration

Custom Processor Configuration Workflow
加速cust的评估和优化om deep learning processor by configuring parameters of theconv processorandfc processor, created by using thedlhdl.ProcessorConfigobject 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.
Generate Custom Bitstream to Meet Custom Deep Learning Network Requirements
Deploy your custom network that only has layers with the convolution module output format or only layers with the fully connected module output format by generating a resource optimized custom bitstream that satisfies your performance and resource requirements.
Create Deep Learning Processor Configuration for Custom Layers
Create a deep learning processor configuration that includes your custom layers.

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 thedlhdl.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.

Open Live Script

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

Estimate Resource Utilization for Custom Board and Reference Design

Rapidly prototype the deployment of deep learning networks to your custom board by using the estimateResources function. Estimate the resource utilization of the deep learning processor configuration for your custom board. Optimize the integration of custom IP cores and reference design into your system by using the estimateResources function to estimate the resource utilization of your reference design. The synthesis tool that you use must be in the list of tools supported by the SynthesisTool property of the dlhdl.ProcessorConfig object. For a list of supported tools and device families, see SynthesisTool and SynthesisToolChipFamily.

Open Live Script

Register, Validate, and Deploy Custom Hyperbolic Tangent Layer Network to FPGA

Register, validate, and deploy a custom hyperbolic tangent (tanh) layer network by using Deep Learning HDL Toolbox™. To deploy the network with the custom hyperbolic tangent (tanh) layer:

Open Live Script