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Deep Learning with INT8 Optimization on Xilinx Devices

Algorithms, AMD, Articles, Processors, Software, Technical Articles, Tools /

This is a reprint of a Xilinx-published white paper which is also available here (1 MB PDF). Xilinx INT8 optimization provide the best performance and most power efficient computational techniques for deep learning inference. Xilinx's integrated DSP architecture can achieve 1.75X solution-level performance at INT8 deep learning operations than other FPGA DSP architectures. ABSTRACT The […]

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“Semantic Segmentation for Scene Understanding: Algorithms and Implementations,” a Presentation from Auviz Systems

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Nagesh Gupta, Founder and CEO of Auviz Systems, presents the "Semantic Segmentation for Scene Understanding: Algorithms and Implementations" tutorial at the May 2016 Embedded Vision Summit. Recent research in deep learning provides powerful tools that begin to address the daunting problem of automated scene understanding. Modifying deep learning methods, such as CNNs, to classify pixels

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“How Computer Vision Is Accelerating the Future of Virtual Reality,” a Presentation from AMD

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Allen Rush, Fellow at AMD, presents the "How Computer Vision Is Accelerating the Future of Virtual Reality" tutorial at the May 2016 Embedded Vision Summit. Virtual reality (VR) is the new focus for a wide variety of applications including entertainment, gaming, medical, science, and many others. The technology driving the VR user experience has advanced

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Optimizing Computer Vision Applications Using OpenCL and GPUs

Algorithms, AMD, Arm, Articles, Imagination Technologies, Processors, Software, Technical Articles, Tools /

The substantial parallel processing resources available in modern graphics processors makes them a natural choice for implementing vision-processing functions. The rapidly maturing OpenCL framework enables the rapid and efficient development of programs that execute across GPUs and other heterogeneous processing elements within a system. In this article, we briefly review parallelism in computer vision applications,

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Accelerating Machine Learning: Implementing Deep Neural Networks on FPGAs

Algorithms, AMD, Processors, Software, Technical Articles, Tools /

This introductory article discusses implementing machine learning algorithms on FPGAs, achieving significant performance improvements at much lower power. Newly available middleware IP, together with the SDAccel programming environment, enables software developers to implement convolutional neural networks (CNNs) in C/C++, leveraging an OpenCL platform model. Machine Learning in the Cloud: A Tipping Point The transformation of

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OpenCL Streamlines FPGA Acceleration of Computer Vision

Algorithms, AMD, Articles, Intel, Processors, Software, Technical Articles, Tools /

The substantial resources available in modern programmable logic devices, in some cases including embedded processor cores, makes them strong candidates for implementing vision-processing functions. The rapidly maturing OpenCL framework enables the rapid and efficient development of programs that execute across programmable logic fabric and other heterogeneous processing elements within a system. As mentioned in the

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“Trade-offs in Implementing Deep Neural Networks on FPGAs,” a Presentation from Auviz Systems

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Nagesh Gupta, CEO and Founder of Auviz Systems, presents the "Trade-offs in Implementing Deep Neural Networks on FPGAs" tutorial at the May 2015 Embedded Vision Summit. Video and images are a key part of Internet traffic—think of all the data generated by social networking sites such as Facebook and Instagram—and this trend continues to grow.

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“Understanding Adaptive Machine Learning Vision Algorithms and Implementing Them on GPUs and Heterogeneous Platforms,” a Presentation from AMD

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Harris Gasparakis, OpenCV Manager at AMD, presents the "Understanding Adaptive Machine Learning Vision Algorithms and Implementing them on GPUs and Heterogeneous Platforms" tutorial at the May 2015 Embedded Vision Summit. Machine learning algorithms are pervasive in computer vision: from object detection to object tracking to full scene recognition, generative or discriminative learning dominates the space,

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“Implementing Eye Tracking for Medical, Automotive and Headset Applications,” a Presentation From Xilinx and EyeTech Digital Systems

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Dan Isaacs, Director of Smarter Connected Systems at Xilinx, and Robert Chappell, Founder of EyeTech Digital Systems, co-present the "Implementing Eye Tracking for Medical, Automotive and Headset Applications" tutorial at the May 2015 Embedded Vision Summit. When humans communicate with each other, we get important cues from watching each other’s eyes. Similarly, machines can gain

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Smart Exterior Cameras Enhance Vehicle Safety, Security

AMD, Articles, Automotive, Intel, NXP Semiconductors, Processors, Sensors and Cameras, Technical Articles, Texas Instruments, videantis /

This article was originally published at John Day's Automotive Electronics News. It is reprinted here with the permission of JHDay Communications. Cameras installed in various locations around a vehicle exterior, in combination with cost-effective, powerful and energy-efficient processors, deliver numerous convenience and protection benefits. The advanced driver assistance systems (ADAS) market is one of the

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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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