Processors

“Implementing Image Pyramids Efficiently in Software,” a Presentation from Polymorphic Technologies

Michael Stewart, Proprietor of Polymorphic Technologies, presents the “Implementing Image Pyramids Efficiently in Software,” tutorial at the May 2018 Embedded Vision Summit. An image pyramid is a series of images, derived from a single original image, wherein each successive image is at a lower resolution than its predecessors. Image pyramids are widely used in computer […]

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“NovuTensor: Hardware Acceleration of Deep Convolutional Neural Networks for AI,” a Presentation from NovuMind

Miao (Mike) Li, Vice President of IC Engineering at NovuMind, presents the “NovuTensor: Hardware Acceleration of Deep Convolutional Neural Networks for AI” tutorial at the May 2018 Embedded Vision Summit. Deep convolutional neural networks (DCNNs) are driving explosive growth of the artificial intelligence industry. Effective performance, energy efficiency and accuracy are all significant challenges in

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“The Journey and Sunrise Processors: Leading-Edge Performance for Embedded AI,” a Presentation from Horizon Robotics

Kai Yu, Founder and CEO of Horizon Robotics, presents the “Journey and Sunrise Processors: Leading-Edge Performance for Embedded AI” tutorial at the May 2018 Embedded Vision Summit. As the nature of computation changes from logic to artificial intelligence, there’s a revolution happening at the edge. A new type of processor is required for this post-Moore’s-law

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“Deep Learning on Arm Cortex-M Microcontrollers,” a Presentation from Arm

Vikas Chandra, Senior Principal Engineer and Director of Machine Learning at Arm, presents the “Deep Learning on Arm Cortex-M Microcontrollers” tutorial at the May 2018 Embedded Vision Summit. Deep learning algorithms are gaining popularity in IoT edge devices due to their human-level accuracy in many tasks, such as image classification and speech recognition. As a

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“Reduce Risk in Computer Vision Design: Focus on the User,” a Presentation from Twisthink

Paul Duckworth, Director of Engineering at Twisthink, presents the “Reduce Risk in Computer Vision Design: Focus on the User” tutorial at the May 2018 Embedded Vision Summit. Companies across a wide range of industries are considering ways to apply computer vision to innovate their products and services. With the vast potential of this exciting technology,

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“Neural Network Compiler: Enabling Rapid Deployment of DNNs on Low-Cost, Low-Power Processors,” a Presentation from Cadence

Megha Daga, Senior Technical Marketing Manager at Cadence, presents the “Neural Network Compiler: Enabling Rapid Deployment of DNNs on Low-Cost, Low-Power Processors” tutorial at the May 2018 Embedded Vision Summit. The use of deep neural networks (DNNs) has accelerated in recent years, with DNNs making their way into diverse commercial products. But DNNs consume vast

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“Enabling Software Developers to Harness FPGA Compute Accelerators,” a Presentation from Intel

Bernhard Friebe, Senior Director of Marketing for the Programmable Solutions Group at Intel, presents the “Enabling Software Developers to Harness FPGA Compute Accelerators” tutorial at the May 2018 Embedded Vision Summit. FPGAs play a critical part in heterogeneous compute platforms as flexible, reprogrammable, multi-function accelerators. They enable custom-hardware performance with the programmability of software. The

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“Leveraging Cloud Computer Vision for a Real-time Consumer Product,” a Presentation from Cocoon Cam

Pavan Kumar, Co-founder and CTO at Cocoon Cam, presents the "Leveraging Cloud Computer Vision for a Real-time Consumer Product" tutorial at the May 2018 Embedded Vision Summit. The capabilities of cloud computing are expanding rapidly. At the same time, cloud computing costs are falling. This makes it increasingly attractive to implement computer vision in the

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“Approaches for Energy Efficient Implementation of Deep Neural Networks,” a Presentation from MIT

Vivienne Sze, Associate Professor at MIT, presents the “Approaches for Energy Efficient Implementation of Deep Neural Networks” tutorial at the May 2018 Embedded Vision Summit. Deep neural networks (DNNs) are proving very effective for a variety of challenging machine perception tasks. But these algorithms are very computationally demanding. To enable DNNs to be used in

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“Understanding Automotive Radar: Present and Future,” a Presentation from NXP Semiconductors

Arunesh Roy, Radar Algorithms Architect at NXP Semiconductors, presents the “Understanding Automotive Radar: Present and Future” tutorial at the May 2018 Embedded Vision Summit. Thanks to its proven, all-weather range detection capability, radar is increasingly used for driver assistance functions such as automatic emergency braking and adaptive cruise control. Radar is considered a crucial sensing

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Here you’ll find a wealth of practical technical insights and expert advice to help you bring AI and visual intelligence into your products without flying blind.

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