Su Li, Senior Technical Account Manager at Horizon Robotics, delivers a product demonstration at the May 2018 Embedded Vision Summit. Specifically, Li demonstrates the company’s smart retail solution using an AI-based face recognition camera, which can capture a maximum of 200 faces per frame, detect at 1080p full-frame rate and identify the age and gender […]
Darren Odom, CEO of Boulder AI, delivers a product demonstration at the May 2018 Embedded Vision Summit. Specifically, Odom demonstrates DNNCam, an intelligent GPU-enabled deep-learning neural network camera that is waterproof, dust-proof and runs AI algorithms at the image source. The Boulder AI edge camera executes AI/machine learning frameworks and computer vision algorithms without additional
Boulder AI Demonstration of Its GPU-enabled DNNCam (Deep Neural Network Camera) Read More +
Ali Osman Ors, Director of AI Strategy and Partnerships for Automotive at NXP Semiconductors, delivers a product demonstration at the May 2018 Embedded Vision Summit. Specifically, Ors demonstrates deep learning-based multi-object detection based on MobileNet and Single Shot Detector (SSD) running in real-time on the embedded automotive-grade S32V234 Vision SoC, with the capability of detecting
Markus Levy, Director of Enabling Technologies at NXP Semiconductors, delivers a product demonstration at the May 2018 Embedded Vision Summit. Specifically, Levy demonstrates the i.MX RT architecture, which represents the convergence of low-power application processors and high-performance microcontrollers. This particular demo shows the i.MX RT1050 MCU, which is based on a 600MHz Arm® Cortex®-M7 core
Ferenc Pintér, Head of Software at AImotive, provides a company update at the May 2018 Embedded Vision Summit. Specifically, Pintér explains how AImotive‘s technological approach and development pipeline support the creation of scalable autonomous solutions. After discussing the fundamental effect of simulation of self-driving development, Pintér introduces aiSim and aiWare, two AImotive technologies and winners
AImotive Scales Toward Productization of Self-Driving Technology Read More +
Richard Chuang, Global Platform Solutions Architect at Intel, delivers a product demonstration at the May 2018 Embedded Vision Summit. Specifically, Chuang demonstrates an end-to-end face detection and recognition reference solution using the OpenVINO toolkit. Four primary algorithms are running in this demo system on top of OpenVINO: face detection, landmark detection, feature extraction, and face
Soren Knudsen, Video and Software Ecosystem Accelerant at Intel, delivers a product demonstration at the May 2018 Embedded Vision Summit. Specifically, Knudsen demonstrates how you can get top performance for vision solutions using Intel’s OpenVINO™ toolkit (open visual inference and neural network optimization), part of the Intel® Vision Products portfolio. View heterogeneous workload performance across
Jeffrey McAllister, Technical Consulting Engineer at Intel, delivers a product demonstration at the May 2018 Embedded Vision Summit. Specifically, McAllister demonstrates how with Intel’s OpenVINO™ toolkit, (open visual inference and neural network optimization), you can boost workload performance with easy heterogeneous execution across multiple types of Intel® platforms (CPU, CPU with integrated graphics, FPGAs, and
Abdullah Raouf, Marketing Manager for Low Power Solutions at Lattice Semiconductor, delivers a product demonstration at the May 2018 Embedded Vision Summit. Specifically, Raouf demonstrates what an ultra-small (2.15 mm x 2.55 mm), $1 USD FPGA is able to do when embedded with the the company’s sensAI BNN accelerator IP. With the demonstrated solution, system
Lattice Semiconductor Demonstration of a Deep Neural Network that Consumes Only ~800 uW Read More +
JP Singh, Automotive Marketing manager at Lattice Semiconductor, delivers a product demonstration at the May 2018 Embedded Vision Summit. Specifically, Singh demonstrates a machine learning CNN implemented using the company’s sensAI stack on a ECP5 FPGA. Singh shows the detection of speed limit signs and the determination of speed limits with a CNN implementation that
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