Enabling Embedded Vision Neural Network DSPs


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


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


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


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


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


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

Teaching Computers To See


Vision processing is emerging as a foundation technology for a number of high-growth applications, spurring a wave of intensive research to reduce power, improve performance, and push embedded vision into the mainstream to leverage economies of scale. What began as a relatively modest development effort has turned into an all-out race for a piece of this market, and for good reason. Mark... » read more

Seeing The Future Of Vision


Vision systems have evolved from cameras that enable robots to “see” on a factory floor to a safety-critical element of the heterogeneous systems guiding autonomous vehicles, as well as other applications that call for parallel processing technology to quickly recognize objects, people, and the surrounding environment. Automotive electronics and mobile devices currently dominate embedded... » read more

Rethinking Processor Architectures


The semiconductor industry's obsession with clock speeds, cores and how many transistors fit on a piece of silicon may be nearing an end for real this time. The [getentity id="22048" comment="IEEE"] said it will develop the International Roadmap for Devices and Systems (IRDS), effectively setting the industry agenda for future silicon benchmarking and adding metrics that are relevant to specifi... » read more

Embedded Vision Becoming Ubiquitous


Embedded vision is becoming a topic of heated conversation thanks to the emergence of neural networks and their ability to make computer systems learn by example. Neural networks are a very different kind of processing element compared to the other kind of processors we have in the IP arsenal today in that they are not programmed in the same manner. They do not have a stream of instructions... » read more

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