TEXAS INSTRUMENTS DEEP LEARNING (TIDL) GOES HERE FOR SITARA PROCESSORS GOES HERE
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1 YOUR TEXAS INSTRUMENTS VIDEO TITLE DEEP LEARNING (TIDL) GOES HERE FOR SITARA PROCESSORS OVERVIEW THE SUBTITLE GOES HERE
2 Texas Instruments Deep Learning (TIDL) for Sitara Processors Overview
3 Texas Instruments Deep Learning (TIDL) for Sitara Processors: Agenda AM57x SoC (System on Chip), AM5749 SoC, and Embedded Vision Engine (EVE) Subsystem TI Deep Learning (TIDL) development flow TIDL on AM57x SoC Validated framework and network models TIDL example use cases TI Design on TIDL: TIDEP-01004
4 AM57x Cortex-A15 Processor Highest integrated, generally available ARM Cortex-A15 in the market Benefits: Unmatched performance in the class Scalable family with pin-compatible single and dual core devices with single Processor SDK for all Sitara devices AM57x Processor Family: SW-compatible AM5746 2x A15 1.5GHz 2x C66x 750MHz AM5748 2x A15 1.5GHz 2x C66x 750MHz 2x 3D, 2D, HW Video AM5749 2x A15 1.5GHz 2x C66x 750MHz 2x 3D, 2D, HW Video 2x EVE-DLA AM5726 2x A15 1.5GHz 2x C66x 750MHz AM5728 2x A15 1.5GHz 2x C66x 750MHz 2x 3D, 2D, HW Video Software & development tools: Processor SDK with LTS Linux TIDL supported by the SDK TI GP EVM and Industrial Development Kit (IDK) available. Performance AM5716 1x A15 1.5GHz/500MHz 1x C66x 750MHz/500MHz AM5706 1x A15 1GHz/500MHz 1x C66x 750/ 500MHz Multimedia AM5718 1x A15 1.5GHz 1x C66x 750MHz 1x 3D, 2D, HW Video AM5708 1x A15 1GHz 1x C66x 750MHz 1x 3D, 2D, HW Video
5 Embedded Vision Engine (EVE) 32-bit RISC processor (ARP32) Deep Learning Accelerator (DLA) 512-bit Vector CO-Processor (VCOP) 16x 16-bit MAC/cycle 10.4 GMAC/sec (or 20.8 GOP/sec) per EVE subsystem at 650 MHz AM5749 2x A15 1.5GHz 2x C66x 750MHz 2x 3D, 2D, HW Video 2x EVE-DLA AM5749 EVE supports 20.8 GMAC/sec at 650 MHz
6 TI Deep Learning (TIDL) development flow Training Inference Trained Network Deep Learning/CNN training Caffe* *Only *Only certain certain models models are supported. supported. There There are are constraints constraints on on supported supported layers layers that that must must be met. be met. This is not a universal translator. * TensorFlow* Caffe-Jacinto * Training (Desktop/Cloud) Device translation tool TIDL Import Translation (Desktop Linux/Arm Linux) TIDL-Inference TIDL_API OpenCL TIDL Library Inference embedded deployment (Sitara AM57x)
7 TI Deep Learning (TIDL) on AM57x SoC TIDL is set of software components that enables a Deep Learning inference at the edge. TIDL is available for free as part of Processor SDK Linux. TIDL runs on all AM57x devices: Runs on EVE subsystem and/or C66x DSP cores AM5749 is the highest performance TIDL device (2x EVE) currently available from TI. NOTE: TIDL runs 1.5x-4x faster on EVE subsystems and consumes less power compared to C66x Fully loaded 2x EVE consumes 220 mw compared to 2x C66x cores which consumes 520 mw. The initial TIDL release: Supports Convolution Neural Networks (CNN) for spatial input data (video, images, ToF, Radar, etc.) Does not support Recurrent Neural Networks (RNN), Long-Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) model used for time-variant data (speech, machine data, etc.) NOTE: Support for RNN/LSTM/GRU is planned for future releases TIDL APIs are used for programming of EVE and DSP cores.
8 TI Deep Learning (TIDL) network models TIDL provides several TI network models: Object classification Object detection Pixel-level semantic segmentation TI s network models and TIDL leverage the following for embedded performance optimization: Efficient CNN configurations/network pruning Sparsity Fixed-point dynamic quantization Classification Classification + Detection Semantic Segmentation These tools result in major computational and bandwidth reductions with minor decrease in accuracy. Cat
9 Validated framework and network models Framework Network Comment BVLC-Caffe * SqueezeNet 1.1 Supports floating-point and on-the-fly 8-bit quantized model TensorFlow * InceptionNet V1 (GoogleNet) Mobilenet 1.0 Supports floating-point and on-the-fly 8-bit quantized model JacintoNet11 Object classification network CAT Caffe-Jacinto JDetNet SSD-based object detection JSegNet21 Pixel-wise semantic segmentation * Only certain neural network models are supported with BVLC-Caffe and TensorFlow frameworks. Certain constraints on layer parameters must be met in order for TI device translator tool to work on it. Details:
10 TI Deep Learning (TIDL) example use cases TIDL Library 5.0 and later Spatial 2D data (RGB sensor, ToF, radar/mmwave, etc.) Classification + Localization Object detection Semantic Segmentation Example Applications Vision computers, optical inspection, semiconductor test and manufacturing Building automation Automated Sorting Equipment, industrial robots ATMs, currency counters Vacuum robots, robotic lawn mowers Smart appliances Role of deep learning Classify products as good or defective. Deep learning can improve accuracy and flexibility. Can track/identify/count people and objects Ability to recognize objects and guide movement/placement Determine valid/counterfeit currency Improve ability to recognize obstacles and things like power cords/wires. Recognize objects in refrigerator, determine food & auto cook
11 TIDL TI Design: TIDEP Features Embedded deep learning inference on AM57x SoC Performance scalable TI deep learning library (TIDL library) on AM57x using C66x DSP only, EVE only, or DSP + EVE. Performance optimized reference CNN models for object classification, detection and pixel-level semantic segmentation. Walk-through of TIDL development flow: training, import and deployment. Benchmarks of several popular deep learning networks on AM5749 This reference design is tested and includes TIDL library on DSP and EVE, reference CNN models and Getting Started Guide. Applications Machine Vision: Vision Computer, Code Readers Automated Machinery: Automated Sorting Equipment, Optical Inspection EPOS: ATMs, Currency Counter Logistics Robots: Logistics Robots CPU board Many others Tools & Resources Benefits Guide through dense and sparse model training, importing and deploying the model for inference run on AM57x SoC. Highly integrated solution bringing deep learning inference to the edge. Example trained networks ready to run TIDL API to run multiple (same or different) networks in parallel on different EVE subsystem and DSP cores. Design Guide Design Files: Schematics, BOM, Gerbers, Software, etc. Device Datasheets: AM5749 IDK (not yet public) AM5749 SoC (not yet public)
12 For more information Sitara AM57x Processors: TI Deep Learning on Processor SDK Linux: TIDL API: Caffe-Jacinto training framework: Caffe Models trained by TI: For questions regarding topics covered in this training, visit the support forums at the TI E2E Community website.
13 TI Information Selective Disclosure
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