深度学习处理器定制和IP生成

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`设置模块属性的方法`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`	通过使用生成的自定义层验证模型来验证自定义层的功能和准确性

话题

自定义处理器配置

自定义处理器配置Workflow
通过配置参数来加速自定义深度学习处理器的估计和优化Conv处理器和FC处理器, 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.
估算自定义处理器配置的资源利用
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.
生成自定义BITSTREAMto 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.
为自定义图层创建深度学习的处理器配置
创建一个深度学习处理器配置，其中包括您的自定义层。

自定义处理器代码生成

生成自定义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.

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Customize Bitstream Configuration to Meet Resource Use Requirements

用户希望将目标性能每秒500帧（FPS）部署到Xilinx™ZCU102 ZU4CG设备。目标设备资源计数是：

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Estimate Resource Utilization for Custom Board and Reference Design

快速原型使用估算材料功能将深度学习网络的部署到您的自定义板上。估计自定义板的深度学习处理器配置的资源利用。通过使用估算功能来估计参考设计的资源利用，将自定义IP内核和参考设计的集成到系统中。您使用的合成工具必须在dlhdl.processorconfig对象的合成属性支持的工具列表中。万博1manbetx有关支持的工具和设备系列的列万博1manbetx表，请参见SyntheSistool和SynthesistoolChipFamily。

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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:

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