CEVA Deep Neural Network (CDNN) A comprehensive inferencing graph compiler for SensPro, NeuPro and CEVA-XM processors OVERVIEW The CEVA Deep Neural Network (CDNN) is a comprehensive compiler technology that creates fully-optimized runtime software for SensPro sensor hub DSPs, NeuPro AI processors and CEVA-XM Vision DSPs. Targeted for mass-market embedded devices, CDNN incorporates a broad range of network optimizations, advanced quantization algorithms, data flow management and fully-optimized compute CNN and RNN libraries into a holistic solution that enables cloud-trained AI models to be deployed on edge devices for inference processing. The CDNN compiler enable an extremely simple and streamlined transition of existing deep neural networks to an embedded environment. NeuPro AI processor ensure superior performance with minimal power consumption. Separately, each component of the CDNN compiler is a powerful enabler of embedded imaging and vision applications. Combined, these pieces deliver an ultimate toolkit to support new network structures and changing layer types of deep neural networks. CEVA supplies a full development platform for partners and developers based on the SensPro, NeuPro and CEVA-XM architectures to enable the development of deep learning applications using the CDNN, targeting any advanced network. BENEFITS The CDNN compiler streamlines implementations of deep learning in embedded systems by automatically quantizing and optimizing offline pre-trained neural networks to real-time embedded-ready networks for SensPro, NeuPro and CEVA-XM cores and customer neural network engines. This enables real-time, high-quality image classification, object recognition, and segmentation, significantly reducing time-to-market for running low power machine learning in embedded systems Automatic quantization and conversion to embedded-ready networks Greatly reduces memory bandwidth for any network via various mechanisms including layer fusion and compression Enables heterogeneous computing architectures, optimizes for and enables seamless utilization of custom AI engines MAIN FEATURES CDNN Compiler converts pre-trained neural network models and weights from offline training frameworks (such as Caffe or TensorFlow) to real-time network models CDNN Run-Time software accelerates deployment of machine learning in low-power embedded processors CDNN-Invite API enables seamless incorporation and usage of custom AI engines within the CDNN framework Product Note BLOCK DIAGRAMS CEVA Deep Neural Network Software Framework Named “2017 Most Innovative Product” by Embedded Computing Design CEVA Wins China Electronics Market (CEM) Editor’s Choice Award for CDNN CEVA CDNN LIVE DEMONSTRATION The CEVA Deep Neural Network (CDNN) is a comprehensive inferencing graph compiler that simplifies the development and deployment of deep learning systems for mass-market embedded devices. CDNN incorporates a broad range of network optimizations, advanced quantization algorithms, data flow management and fully-optimized compute CNN and RNN type networks into a holistic solution that enables cloud-trained AI models to be deployed on edge devices for inference processing on low device. CDNN enables an extremely simple and streamlined transition of existing deep neural networks to power optimized and memory constrained embedded environments. CDNN enables heterogeneous computing and is flexible to split a network between multiple compute engines such as SensPro or NeuPro processors and custom AI Engines, all this to ensure and optimize for superior performance with minimal power consumption. CDNN is composed of an offline compiler and run-time components. While the CDNN compiler quantized and optimizes the networks, the CDNN run-time executes the network in real-time on the various processors. Combined, these pieces deliver an ultimate toolkit to support variety of network structures today and robust for supporting the changing layer types and structures of deep neural networks going forward. CEVA supplies a full development platform for partners and developers based on the CEVA-XM and NeuPro architectures as well as custom neural network engines via the CDNN-Invite API. These enable the development of deep learning applications using the CDNN, targeting any advanced network. RESOURCES AND DOWNLOADS PRODUCT NOTE PDF CDNN Product Note - English PDF CDNN Product Note - Chinese PDF CDNN Product Note - Japanese PRESENTATIONS Presentation Efficient Implementation of Neural Networks Presentation CEVA-XM6 5th Generation Vision Platform Presentation DEMOS Video Real-time Face Detection Neural Network Video Video Real-time Tiny Yolo Neural Network Video Video CEVA CDNN2 live demonstration Video Real-Time CNN Object Recognition Video WEBINAR Webinar Webinar: Deep Learning on Embedded Systems: 7 minutes could save you 7 months or more of hand porting and optimization Presentation Webinar Presentation: Deep Learning on Embedded Systems: 7 minutes could save you 7 months or more of hand porting and optimization RELATED PRODUCTS Application Developer Kit The Application Developer Kit (ADK) for CEVA-XM, SensPro2 and NeuPro streamlines the software development and integration effort required for advanced vision and AI applications.... Wide Angle Imaging Immervision patented image processing algorithms facilitate dramatic improvements in image quality and remove inherent distortions associated with the use of wide-angle cameras,... CEVA-XM4 The CEVA-XM4™ imaging and computer vision processor IP solves the most critical issues for the development of energy-efficient embedded vision systems where die size and... CEVA-XM6 The CEVA-XM6™ is a fifth-generation imaging and computer vision processor IP from CEVA, and is designed to bring deep learning and artificial intelligence capabilities to... CEVA NeuPro-S NeuPro-S™ is a low power AI processor architecture for on-device deep learning inferencing, imaging and computer vision workloads. While NeuPro-S provides a... Data in Picture Immervision proprietary Data-in-Picture software IP integrates contextual and visual data from disparate sources into image pixels, synchronized with each image and video frame.... 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