Deep Learning Processor Customization and IP Generation
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.
Custom Board Calibration
Performance and Resource Estimation
|Retrieve layer-level latencies and performance by using
estimatePerformance method (Since R2021a)
|Return estimated resources used by custom bitstream configuration (Since R2021a)
|Update network-specific deep learning processor configuration with optimized deep learning processor configuration (Since R2021b)
Custom Layer Support
|Open a generated custom layer verification model to verify your custom layers (Since R2022a)
|Register the custom layer definition and Simulink model representation of the custom layer (Since R2022a)
|Verify the functionality and accuracy of the custom layer by using the generated custom layer verification model (Since R2022a)
Custom Processor Configuration
- Custom Processor Configuration Workflow
Accelerate the estimation and optimization of custom deep learning processor by configuring parameters of the
fc processor, created by using the
- Deep Learning Processor IP Core Architecture
Learn about the FPGA architecture based custom deep learning processor architecture and using it to create a MATLAB® controlled deep learning processor.
- 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.