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.
dlhdl.ProcessorConfig | Configure custom deep learning processor |
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 |
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.
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.
Build and generate IP for the dlhdl.ProcessorConfig