Computational Imaging Craves System-Level Design And Simulation Tools To Leverage Disruptive AI In Embedded Vision

By Synopsys - 24 May, 2023 - Comments: 0

Image quality now relies more than ever on high computing power tied to miniaturized optics and sensors, rather than on standalone and bulky but aberration-free optics. This new trend is called computational imaging and can be used either for computational photography or for computer vision. Read this white paper to learn about market trends and promising system co-design and co-optimization ap... » read more

Startup Funding: March 2020

By Jesse Allen - 08 Apr, 2020 - Comments: 0

Dedicated AI hardware, quantum computing, and avionics startups shined in March. Here's a look at seventeen startups, which raised a collective $525M. The avionics sector soared thanks to Lilium and its electric vertical takeoff jet. Quantum computing was another hot area, with three companies bringing in ~$88M together. Plus, chip design management, two companies developing AR glasses, and how... » read more

PowerPR Virtualization: A Critical Feature For Automotive GPUs

By Imagination Technologies - 26 Feb, 2020 - Comments: 0

What is GPU virtualization? Conceptually, virtualization is the capability of a device to host one or more virtual machines (VMs) that each behave like actual independent machines with their own operating system (OS), all running on the same underlying device hardware. In regard to GPUs, this means the capability to support multiple concurrently running operating systems, each capable of submit... » read more

Tradeoffs In Embedded Vision SoCs

By Ed Sperling - 28 Jan, 2020 - Comments: 0

Gordon Cooper, product marketing manager for embedded vision processors at Synopsys, talks with Semiconductor Engineering about the need for more performance in these devices, how that impacts power, and what can be done to optimize both prior to manufacturing. » read more

Enabling Embedded Vision Neural Network DSPs

By Cadence - 19 Dec, 2018 - Comments: 0

Neural networks are now being developed in a variety of technology segments in the embedded market, from mobile to surveillance to the automotive segment. The computational and power requirements to process this data is increasing, with new methods to approach deep learning challenges emerging every day. Vision processing systems must be designed holistically, for all platforms, with hardwa... » read more

eFPGAs Offer Practical Solution For Embedded Vision Applications

By Alok Sanghavi - 06 Dec, 2018 - Comments: 0

Video applications, such as surveillance, object detection and motion analysis, rely on 360° embedded vision and high-resolution fish-eye cameras lenses with a wide-angle field of view (FOV). These systems have up to six real-time camera streams processing together frame by frame. Each frame is corrected for distortion and other image artifacts, adjusted for exposure and white balance, then st... » read more

The Week In Review: Design

By Jesse Allen - 13 Apr, 2018 - Comments: 0

Tools & IP Cadence unveiled its latest DSP for embedded vision and AI, Tensilica Vision Q6 DSP. The DSP is built on a 13-stage processor pipeline and new system architecture designed for use with large local memories, and achieves 1.5GHz peak frequency and 1GHz typical frequency at 16nm. Compared to its predecessor, it offers 1.5X greater vision and AI performance than its predecessor and ... » read more

Software Framework Requirements For Embedded Vision

By Gordon Cooper - 09 Nov, 2017 - Comments: 0

Deep learning techniques such as convolutional neural networks (CNN) have significantly increased the accuracy—and therefore the adoption rate—of embedded vision for embedded systems. Starting with AlexNet’s win in the 2012 ImageNet Large Scale Visual Recognition Challenge (ILSVRC), deep learning has changed the market by drastically reducing the error rates for image classification and d... » read more

The Evolution Of Deep Learning For ADAS Applications

By Gordon Cooper - 08 Jun, 2017 - Comments: 0

Embedded vision solutions will be a key enabler for making automobiles fully autonomous. Giving an automobile a set of eyes – in the form of multiple cameras and image sensors – is a first step, but it also will be critical for the automobile to interpret content from those images and react accordingly. To accomplish this, embedded vision processors must be hardware optimized for performanc... » read more

LiDAR Completes Sensing Triumvirate

By Jeff Dorsch - 06 Apr, 2017 - Comments: 3

Fully autonomous vehicles of the future will depend on a combination of different sensing technologies – advanced vision systems, radar, and light imaging, detection, and ranging (LiDAR). Of the three, LiDAR is now the costliest part of that equation, and there are worldwide efforts to bring down those costs. Mechanical LiDAR units are currently available, priced in the hundreds of dollars... » read more

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Entities, people and technologies explored

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3.5D: The Great Compromise

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eBook: Nearly everything you need to know about memory, including detailed explanations of the different types of memory; how and where these are used today; what's changing, which memories are successful and which ones might be in the future; and the limitations of each memory type.

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What Comes After HBM For Chiplets

The standard for high-bandwidth memory limits design freedom at many levels, but that is required for interoperability. What freedoms can be taken from other functions to make chiplets possible?