"Fast Inference in Low Power Systems via CEVA's Deep Neural Network Solution," a Presentation from CEVA
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For the full video of this presentation, please visit: https://www.embedded-vision.com/platinum-members/ceva/embedded-vision-training/videos/pages/may-2017-embedded-vision-summit For more information about embedded vision, please visit: http://www.embedded-vision.com Yair Siegel, Director of Strategic Marketing at CEVA, presents the "Fast Inference in Low Power Systems via CEVA’s Deep Neural Network Solution" tutorial at the May 2017 Embedded Vision Summit. The emergence of state-of-the-art, real-time object detection solely based on convolutional neural networks has created new and complex challenges for embedded systems. Algorithms such as Faster R-CNN, YOLO and SSD are performance-intensive and require high data bandwidth, but embedded implementations have extreme resource limitations. Various processors and hardware accelerators offer potential solutions, but entail highly complex software development and achieving optimal performance with them requires an acute understanding of how to distribute the workload across the various processing units. This presentation outlines the challenges of implementing high-precision, advanced neural networks for embedded vision, and explains how using CEVA’s automatic software tools in combination with a mix of processing units can achieve a power efficient, flexible and very fast time-to-market solution for inferencing in low-cost production systems.
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Spyros Tragoudas, Professor and School Director at Southern Illinois University Carbondale, presents the “Detecting Data Drift in Image Classification Neural Networks” tutorial at the May 2023 Embedded Vision Summit.
An unforeseen change in the input data is called “drift,” and may impact the accuracy of machine learning models. In this talk, Tragoudas presents a novel scheme for diagnosing data drift in the input streams of image classification neural networks. His proposed method for drift detection and quantification uses a threshold dictionary for the prediction probabilities of each class in the neural network model.
The method is applicable to any drift type in images such as noise and weather effects, among others. Tragoudas shares experimental results on various data sets, drift types and neural network models to show that his proposed method estimates the drift magnitude with high accuracy, especially when the level of drift significantly impacts the model’s performance.“Deep Neural Network Training: Diagnosing Problems and Implementing Solutions...
“Deep Neural Network Training: Diagnosing Problems and Implementing Solutions...Edge AI and Vision Alliance
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2023/10/deep-neural-network-training-diagnosing-problems-and-implementing-solutions-a-presentation-from-sensor-cortek/
Fahed Hassanat, Chief Operating Officer and Head of Engineering at Sensor Cortek, presents the “Deep Neural Network Training: Diagnosing Problems and Implementing Solutions” tutorial at the May 2023 Embedded Vision Summit.
In this presentation, Hassanat delves into some of the most common problems that arise when training deep neural networks. He provides a brief overview of essential training metrics, including accuracy, precision, false positives, false negatives and F1 score.
Hassanat then explores training challenges that arise from problems with hyperparameters, inappropriately sized models, inadequate models, poor-quality datasets, imbalances within training datasets and mismatches between training and testing datasets. To help detect and diagnose training problems, he also covers techniques such as understanding performance curves, recognizing overfitting and underfitting, analyzing confusion matrices and identifying class interaction issues.
“AI Start-ups: The Perils of Fishing for Whales (War Stories from the Entrepr...
“AI Start-ups: The Perils of Fishing for Whales (War Stories from the Entrepr...Edge AI and Vision Alliance
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2023/10/ai-start-ups-the-perils-of-fishing-for-whales-war-stories-from-the-entrepreneurial-front-lines-a-presentation-from-seechange-technologies/
Tim Hartley, Vice President of Product for SeeChange Technologies, presents the “AI Start-ups: The Perils of Fishing for Whales (War Stories from the Entrepreneurial Front Lines)” tutorial at the May 2023 Embedded Vision Summit.
You have a killer idea that will change the world. You’ve thought through product-market fit and differentiation. You have seed funding and a world-beating team. Best of all, you’ve caught the attention of major players in your industry. You’ve reached peak “start-up”—that point of limitless possibility—when you go to bed with the same level of energy and enthusiasm you had when you woke. And then the first proof of concept starts…
In this talk, Hartley lays out some of the pitfalls that await those building the next big thing. Using real examples, he shares some of the dos and don’ts, particularly when dealing with that big potential first customer. Hartley discusses the importance of end-to-end design, ensuring your product solves real-world problems. He explores how far the big companies will tell you to jump—and then jump again—for free. And, most importantly, how to build long-term partnerships with major corporations without relying on over-promising sales pitches.“A Computer Vision System for Autonomous Satellite Maneuvering,” a Presentati...
“A Computer Vision System for Autonomous Satellite Maneuvering,” a Presentati...Edge AI and Vision Alliance
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2023/10/a-computer-vision-system-for-autonomous-satellite-maneuvering-a-presentation-from-scout-space/
Andrew Harris, Spacecraft Systems Engineer at SCOUT Space, presents the “Developing a Computer Vision System for Autonomous Satellite Maneuvering” tutorial at the May 2023 Embedded Vision Summit.
Computer vision systems for mobile autonomous machines experience a wide variety of real-world conditions and inputs that can be challenging to capture accurately in training datasets. Few autonomous systems experience more challenging conditions than those in orbit. In this talk, Harris describes how SCOUT Space has designed and trained satellite vision systems using dynamic and physically informed synthetic image datasets.
Harris describes how his company generates synthetic data for this challenging environment and how it leverages new real-world data to improve our datasets. In particular, he explains how these synthetic datasets account for and can replicate real sources of noise and error in the orbital environment, and how his company supplements them with in-space data from the first SCOUT-Vision system, which has been in orbit since 2021.“Bias in Computer Vision—It’s Bigger Than Facial Recognition!,” a Presentatio...
“Bias in Computer Vision—It’s Bigger Than Facial Recognition!,” a Presentatio...Edge AI and Vision Alliance
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2023/10/bias-in-computer-vision-its-bigger-than-facial-recognition-a-presentation-from-santa-clara-university/
Susan Kennedy, Assistant Professor of Philosophy at Santa Clara University, presents the “Bias in Computer Vision—It’s Bigger Than Facial Recognition!” tutorial at the May 2023 Embedded Vision Summit.
As AI is increasingly integrated into various industries, concerns about its potential to reproduce or exacerbate bias have become widespread. While the use of AI holds the promise of reducing bias, it can also have unintended consequences, particularly in high-stakes computer vision applications such as facial recognition. However, even seemingly low-stakes computer vision applications such as identifying potholes and damaged roads can also present ethical challenges related to bias.
This talk explores how bias in computer vision often poses an ethical challenge, regardless of the stakes involved. Kennedy discusses the limitations of technical solutions aimed at mitigating bias, and why “bias-free” AI may not be achievable. Instead, she focuses on the importance of adopting a “bias-aware” approach to responsible AI design and explores strategies that can be employed to achieve this.“Sensor Fusion Techniques for Accurate Perception of Objects in the Environme...
“Sensor Fusion Techniques for Accurate Perception of Objects in the Environme...Edge AI and Vision Alliance
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2023/10/sensor-fusion-techniques-for-accurate-perception-of-objects-in-the-environment-a-presentation-from-sanborn-map-company/
Baharak Soltanian, Vice President of Research and Development for the Sanborn Map Company, presents the “Sensor Fusion Techniques for Accurate Perception of Objects in the Environment” tutorial at the May 2023 Embedded Vision Summit.
Increasingly, perceptual AI is being used to enable devices and systems to obtain accurate estimates of object locations, speeds and trajectories. In demanding applications, this is often best done using a heterogeneous combination of sensors (e.g., vision, radar, LiDAR). In this talk, Soltanian introduces techniques for combining data from multiple sensors to obtain accurate information about objects in the environment.
Soltanian briefly introduces the roles played by Kalman filters, particle filters, Bayesian networks and neural networks in this type of fusion. She then examines alternative fusion architectures, such as centralized and decentralized approaches, to better understand the trade-offs associated with different approaches to sensor fusion as used to enhance the ability of machines to understand their environment.“Updating the Edge ML Development Process,” a Presentation from Samsara
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2023/10/updating-the-edge-ml-development-process-a-presentation-from-samsara/
Jim Steele, Vice President of Embedded Software at Samsara, presents the “Updating the Edge ML Development Process” tutorial at the May 2023 Embedded Vision Summit.
Samsara (NYSE:IOT) is focused on digitizing the world of operations. The company helps customers across many industries—including food and beverage, utilities and energy, field services and government—get information about their physical operations into the cloud, so they can operate more safely, efficiently and sustainably. Samsara’s sensors collect billions of data points per day and on-device processing is instrumental to its success. The company is constantly developing, improving and deploying ML models at the edge.
Samsara has found that the traditional development process—where ML scientists create models and hand them off to firmware engineers for embedded implementation—is slow and often produces difficult-to-resolve differences between the original model and the embedded implementation. In this talk, Steele presents an alternative development process that his company has adopted with good results. In this process, firmware engineers develop a general framework that ML scientists use to design, develop and deploy their models. This enables quick iterations and fewer confounding bugs.
“Combating Bias in Production Computer Vision Systems,” a Presentation from R...
“Combating Bias in Production Computer Vision Systems,” a Presentation from R...Edge AI and Vision Alliance
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2023/10/combating-bias-in-production-computer-vision-systems-a-presentation-from-red-cell-partners/
Alex Thaman, Chief Architect at Red Cell Partners, presents the “Combating Bias in Production Computer Vision Systems” tutorial at the May 2023 Embedded Vision Summit.
Bias is a critical challenge in predictive and generative AI that involves images of humans. People have a variety of body shapes, skin tones and other features that can be challenging to represent completely in training data. Without attention to bias risks, ML systems have the potential to treat people unfairly, and even to make humans more likely to do so.
In this talk, Thaman examines the ways in which bias can arise in visual AI systems. He shares techniques for detecting bias and strategies for minimizing it in production AI systems.“Developing an Embedded Vision AI-powered Fitness System,” a Presentation fro...
“Developing an Embedded Vision AI-powered Fitness System,” a Presentation fro...Edge AI and Vision Alliance
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2023/10/developing-an-embedded-vision-ai-powered-fitness-system-a-presentation-from-peloton-interactive/
Sanjay Nichani, Vice President for Artificial Intelligence and Computer Vision at Peloton Interactive, presents the “Developing an Embedded Vision AI-powered Fitness System” tutorial at the May 2023 Embedded Vision Summit.
The Guide is Peloton’s first strength-training product that runs on a physical device and also the first that uses AI technology. It turns any TV into an interactive personal training studio. Members have access to Peloton’s instructors, who lead classes that use dumbbells and body weight. The Guide uses an innovative combination of hardware, software and user interface; it is one of only a handful of consumer products in this market that use real-time embedded computer vision, and Peloton’s members love it!
In this talk, Nichani shares insights into how the computer vision technology behind the Guide was built. He focuses on data, models and processes. The AI and Computer Vision team at Peloton worked through numerous obstacles by using synthetic data, advanced algorithms and field-testing iteration to create an affordable AI-powered device that enables at-home fitness for everyone.“Navigating the Evolving Venture Capital Landscape for Edge AI Start-ups,” a ...
“Navigating the Evolving Venture Capital Landscape for Edge AI Start-ups,” a ...Edge AI and Vision Alliance
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2023/10/navigating-the-evolving-venture-capital-landscape-for-edge-ai-start-ups-a-presentation-from-hpe-pathfinder/
Todd Poole, Director of Venture Capital Investments for HPE Pathfinder Ventures, presents the “Navigating the Evolving Venture Capital Landscape for Edge AI Start-ups” tutorial at the May 2023 Embedded Vision Summit.
The venture capital environment has undergone a huge shift in the past year. In this talk, Poole explains how the VC landscape has changed and the implications for start-ups. He examines the impacts on valuations, VC’s willingness to fund, appetite for risk and leverage in deal structuring and terms negotiation.
Pool explores how AI and ML hardware and software companies have been affected differently from the broader start-up ecosystem and considers the distinct challenges faced by start-ups in the edge AI sector. He offers guidance for founders, soon-to-be-founders and start-up employees on how to navigate this dynamic environment and strategize effectively in response to changing market conditions.“Advanced Presence Sensing: What It Means for the Smart Home,” a Presentation...
“Advanced Presence Sensing: What It Means for the Smart Home,” a Presentation...Edge AI and Vision Alliance
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2023/10/advanced-presence-sensing-what-it-means-for-the-smart-home-a-presentation-from-omdia/
Jack Narcotta, Principal Analyst for the Smart Home at Omdia, presents the “Advanced Presence Sensing: What It Means for the Smart Home” tutorial at the May 2023 Embedded Vision Summit.
Eventually, homes will become highly autonomous—powered by ubiquitous, connected, intelligent devices (sometimes referred to as ambient computing)—but this remains a distant vision. In the meantime, advanced presence sensing innovations from component manufacturers, software developers and smart home device companies are enabling meaningful improvements in convenience, efficiency and safety.
Omdia expects advanced presence detection to be adopted rapidly in the coming years, and to become a key factor in how consumers select devices and service providers. In this talk, Narcotta provides an overview of key enabling technologies, market appetite, consumer expectations and long-term outlook for advanced presence sensing in the home.“Tracking and Fusing Diverse Risk Factors to Drive a SAFER Future,” a Present...
“Tracking and Fusing Diverse Risk Factors to Drive a SAFER Future,” a Present...Edge AI and Vision Alliance
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2023/10/tracking-and-fusing-diverse-risk-factors-to-drive-a-safer-future-a-presentation-from-nauto/
Yoav Banin, Chief Product and Business Development Officer, and Tahmida Mahmud, Engineering Manager for Perception, both of Nauto, presents the “Tracking and Fusing Diverse Risk Factors to Drive a SAFER Future” tutorial at the May 2023 Embedded Vision Summit.
Unless you’re a gang member or drug addict, driving is your top risk. But which risks can you handle and which can kill you? Since 96% of collisions are caused by human error and 75% are unconscious (the driver is not aware of the risk), Nauto used one billion miles of real-life driving data to find out, feeding it into a model fusing 26 road context, driver action/attention and vehicle dynamics factors to predict collisions, near misses and safe driving.
Nauto, as described by Banin and Mahmud in this presentation, found that drivers can handle most single risks, but multiple simultaneous risks can be deadly. Some combinations are 1,000 times more dangerous than regular driving. Nauto’s “SAFER” model can be fitted to any late-model vehicle. It can be trained with labeled data from historical trips or through reinforcement learning from observations of excellent and poor drivers. A time series matrix can help you see a few seconds into the future.Essentials of Automations: The Art of Triggers and Actions in FME
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdf
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
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Jim Steele, Vice President of Embedded Software at Samsara, presents the “Updating the Edge ML Development Process” tutorial at the May 2023 Embedded Vision Summit.
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Alex Thaman, Chief Architect at Red Cell Partners, presents the “Combating Bias in Production Computer Vision Systems” tutorial at the May 2023 Embedded Vision Summit.
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Jack Narcotta, Principal Analyst for the Smart Home at Omdia, presents the “Advanced Presence Sensing: What It Means for the Smart Home” tutorial at the May 2023 Embedded Vision Summit.
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- 1. Copyright © 2017 CEVA Inc. 1 Fast Inference in Low Power Systems via CEVA’s Deep Neural Network Solution Yair Siegel, Director of Strategic Marketing May 2017
- 2. Copyright © 2017 CEVA Inc. 2 The Need for Embedded Inferencing & AI Security & Surveillance Automotive Visual Perception & Analytics AR and VR Drones Smartphones & Tablets
- 3. Copyright © 2017 CEVA Inc. 3 • Semantic Segmentation • Automotive free space detection • Bokeh camera effect (single cam) • Object Detection & Recognition • Video search & tagging • Object overlay for AR • Human-Machine Interaction • Driver drowsiness & driver distraction • Foveated rendering / Gaze Detection • Authentication (Face, Iris, Palm) Example Use-Cases for Inferencing
- 4. Copyright © 2017 CEVA Inc. 4 • Neural Networks vs. “Traditional Vision” • Robustness, accuracy, proven, combination • Cloud vs. edge processing • Latency, computation power, data privacy • Growth in network size • Alexnet – 0.6 GMACs per frame • YOLO Tiny – 2.3 GMACs per frame • Battery life and device cost • Opposite requirement to trend • Need for efficient engines and networks Neural Networks Inferencing Considerations
- 5. Copyright © 2017 CEVA Inc. 5 • Performance & Energy State of the art CNN extremely demanding in computation and memory bandwidth, battery envelope steady and limited • Flexibility Deep Neural Networks research keeps evolving • New layer types, topologies frequently added Inferencing Solution Motivation Neural Networks Evolution • R-CNN, LE, Alexnet • Object Identification • Linear Networks • Faster R-CNN • Semantic segmentation • Separate networks for detection and classification • Yolo, SSD, Mask R-CNN • Single stage networks for Identification and Classification
- 6. Copyright © 2017 CEVA Inc. 6 CEVA Deep Neural Network (CDNN) Flow OEM / Partner (offline) CEVA (offline + real-time)
- 7. Copyright © 2017 CEVA Inc. 7 CDNN Toolkit CNN CEVA-CV CEVA-VX Comprehensive toolkit simplifying dev. & deployment of deep learning systems for mass-market inferencing
- 8. Copyright © 2017 CEVA Inc. 8 • Comprehensive inferencing solution • Input: Trained network • Output: Optimal inference network on vision DSP + HW accelerator (optional) CDNN Toolkit – Solution Overview CDNN2 Runtime library CNN HWA Driver CDNN2 Application API CEVA-XMHost CPU CNN HWA CEVA CNN Hardware Accelerator CDNN2 ApplicationAPI Application CDNN2 Network Generator Offline - PC
- 9. Copyright © 2017 CEVA Inc. 9 • CEVA Network Generator • Offline auto converts floating to fixed-point nets • Adapts for embedded constraints • Application API • High level interface for easy integration on host application • Responsible for connectivity between host and DSP • Runtime Library • Executes network inference • Optimized for CEVA-XM vision DSP • On-the-fly bandwidth optimizations • HWA Driver • Controls the CNN HWA CDNN Solution Overview (cont.) CDNN2 Runtime library CNN HWA Driver CDNN2 Application API CEVA-XMHost CPU CNN HWA CEVA CNN Hardware Accelerator CDNN2 ApplicationAPI Application CDNN2 Network Generator Offline - PC
- 10. Copyright © 2017 CEVA Inc. 10 CNN Development Scene • Flexible and future proof, while providing top performance • Combines programmable DSP with hardware acceleration • Hardware accelerator focuses on demanding layers, boosting inference performance up to 12x* • DNN rapidly evolving, yet convolution layers consistently handling main function • Convolution layers are most demanding portion of inference CEVA Deep Neural Network Concept CDNN Solution Concept * vs. CEVA-XM4
- 11. Copyright © 2017 CEVA Inc. 11 Flexible Embedded CNN Solution Flexible embedded solution and 16-bit support required to cope with evolving lead neural networks CEVA-XM Vision DSP CNN Hardware Accelerator • Controls full network execution • Invokes CNN HWA • Executes all other layers: Normalization, Pooling, Deconvolution, Etc. • Supports multiple CNN HWAs CDNN2 Real-Time SW Library • Up to 512 MAC units • 16 b x 16 b support • Executes convolutions • Internal memories • Internal DMA units • Autonomous execution CNN HWA V1
- 12. Copyright © 2017 CEVA Inc. 12 Caffe (32-bit PC) vs. CDNN2 (16-bit Fixed) https://youtu.be/VnbCVFyuWYk Legend Running Deepglint proprietary network Original version CDNN version
- 13. Copyright © 2017 CEVA Inc. 13 Example: Faster R-CNN Challenge Automatic network analysis & optimization via CDNN without user involvement Before High Bandwidth After Low Bandwidth Keeping Bit Accuracy
- 14. Copyright © 2017 CEVA Inc. 14 • Network generator converts network to fixed point • Reduces size and bandwidth by combining layers and other methods • Auto partitions Yolo layers, balanced across multiple HWAs and DSP • Convolutions handled by HWAs • Utilizes local memories, avoids tiling, lowers latency • Yolo Tiny performance • Standalone DSP – 10s frames / sec • Hardware acc. – 100s frames / sec Example: YOLO Network on CDNN Solution utilizes DSP coupled with HWA, controlled via CDNN tools
- 15. Copyright © 2017 CEVA Inc. 15 • 2nd gen SW framework support • Caffe and TensorFlow Frameworks • Various networks* • All network topologies • All the leading layers • Variable ROIs • “Push-button” conversion from pre-trained networks to optimized real-time • Accelerates AI deployment for embedded • Optimized for CEVA-XM DSPs and CNN HW accelerator CEVA Deep Neural Network (CDNN2) (*) Including AlexNet, GoogLeNet, ResNet, SegNet, VGG, NIN and others CDNN2 Wins China Electronics Market (CEM) Editor's Choice Award
- 16. Copyright © 2017 CEVA Inc. 16 • An Analysis of Deep Neural Network Models for Practical Applications • Mask R-CNN + example of Inferencing for Mobile AI (Facebook F8) • CDNN on CEVA web page Come view CDNN live Running Alexnet, Googlenet, Yolo and more, @CEVA booth Additional Resources Live CDNN2 demo
- 17. Copyright © 2017 CEVA Inc. 17 Thank You! Contact: Yairs@ceva-dsp.com
- 18. Copyright © 2017 CEVA Inc. 18 Backup Materials
- 19. Copyright © 2017 CEVA Inc. 19 Corporate Facts Worldwide Operations World Leading IP Supplier Corporate Introduction • Headquartered in Mountain View, Calif. • Publicly traded • NASDAQ:CEVA • Profitable, strong business and cash positive – over $150M cash • Licensing IP since 1991 • Low Power Signal Processing IP for a range of high volume applications: Communications, Wi-Fi, Bluetooth, Audio, Voice, Imaging & Vision, Storage • U.S. (Mountain View, CA, Austin, TX & Detroit, MI), Israel, Ireland, France, U.K, Sweden, China, Taiwan, Korea & Japan
- 20. Copyright © 2017 CEVA Inc. 20 CEVA By Numbers More than 300 licensees to date >8 Billion CEVA-powered devices shipped worldwide to date 100 licensees of Wi-Fi & Bluetooth IP – and more than 1 billion chips shipped 3X the market share in DSP over any other DSP IP vendor 1 in 3 handsets worldwide are powered by CEVA DSP >5 billion DSP cores in audio/voice devices shipped to date >30 licensees for imaging and vision – shipping broadly in 2017
- 21. Copyright © 2017 CEVA Inc. 21 CEVA IP Platforms Wireless (Short Range) Wireless (Long Range) Sensing Intelligence CEVA-XC LTE / 5G SDR CEVA-X LTE / 5G PHY Control LTE-IoT RivieraWaves Wi-Fi 802.11n/ac AP and Client RivieraWaves Bluetooth Dual mode, BLE and Audio over BLE CEVA-X Sense & Connect Hub CEVA-TeakLite-4 Voice Activation CEVA-XM Machine Vision and Deep Learning
- 22. Copyright © 2017 CEVA Inc. 22 • Comprehensive vision IP platform • Centered on CEVA-XM6 Vision DSP • Enables mass market intelligent vision applications • Simplifies deep learning inferencing for low-power embedded devices CEVA’s 5th Generation Imaging & Vision Technology