![]() The fully connected op used in place of GEMM and binary ops.Single-precision floating point (FP32) tensors on all operations.Basic functionality using half-precision floating point (FP16) tensors.Most of the models are versions of MobileNet for image classification that demonstrate the following: The SDK includes eleven sample models that demonstrate how you can use the extension to exploit other features of OpenCL on the Adreno GPU. The output from those tools is data that the sample models in cl_qcom_ml_ops extension use. Graph Model to QFP16/32 Tool - This extracts the weight tensor as '.qfp16' and '.qfp32' file types, which hold half- and full-precision data respectively.The resulting Graph Model retains the source model's topology and weight data. Generate Graph Model Tool - This converts TensorFlow protobuf frozen models (.pb) or TensorFlowLite (.tflite) models into a TensorFlow Graph Model representation.That’s why we’ve provided a pair of tools for extracting and converting weight tensor data from TensorFlow models: With the OpenCL ML SDK, you’re working so close to the silicon that there’s no easy, one-click way to convert models. Use the header files and documentation in the SDK to modify your applications to call new functions in the OpenCL driver we ship with our Android image.Īs for tools, if you’ve used our Qualcomm® Neural Processing SDK, you’re probably familiar with utilities for easily converting, say, a TensorFlow Lite model into another format. Plus, we’ve designed the OpenCL ML extension to enable training of ML models on Adreno soon. Since OpenCL ML is a C-based API, your models are effectively more secure because they do not need to be stored in an interpretable file format.Backing memory can also be reused across tensors, reducing the footprint. Backing memory for tensors is explicitly controlled by the application that means that application controls the tensor memory footprint.To profile ML operations you can use OpenCL events for details like submit times and GPU execution times.For example, uploads of weight data and the dispatch of each ML operation are explicitly initiated by the application. Working lower in the stack offers fine-grained control over memory allocation, data movement, execution and synchronization. The advantages of working lower in the stack Then, dispatch them inline to the same queues with full compatibility. If you have other OpenCL kernels or write custom operations, mix them with operations from our extension. It takes advantage of standard OpenCL features like command queues, buffers and events, and supports FP16 and FP32 data types. The extension is compatible with the OpenCL hooks your applications depend on for features like post-processing, controlling performance and managing memory. Use the extension to implement an ML model as a sequence of linked ML ops, linked by using the same tensor as the appropriate parameter for each op. They execute in line with other OpenCL commands on the same queue, and you can use OpenCL events to track their execution. The extension offers a new take on the concept of those ML ops, each of which is backed by a kernel optimized for the Adreno GPU. Starting with our Adreno 660 GPU in the Snapdragon® 888 mobile platform, the extension is engineered to accelerate the most common image processing and ML ops, including these: Convolution We’ve designed the extension for full compatibility with the kernels you develop using OpenCL and we’ve added the performance that comes from our deep knowledge of our Adreno GPU. (QTI) works with all the major players in the GPU world. This is your chance to keep developing with the industry-standard OpenCL API while moving your ML workloads closer to the silicon and getting higher performance from Adreno.Ĭl_qcom_ml_ops extension plays nice with OpenCLĪs a promoter member of The Khronos Group, Qualcomm Technologies, Inc. So we’ve added an OpenCL extension, cl_qcom_ml_ops, that lets you take advantage of the ML acceleration we’ve built into the OpenCL driver in our Android image. Some of you have told us that you’ve written OpenCL libraries for machine learning, and you’re running them on the Adreno GPU. Are you using OpenCL to run machine learning workloads on the Qualcomm® Adreno™ GPU? Want to optimize your application and improve performance? Download our OpenCL ML SDK and use our OpenCL extension in your development. ![]()
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