Low-Power Deep Learning Implementation For Automotive ICs

Examples of automotive applications abound where high-performance, low-power embedded vision processors are used, from in-car driver drowsiness detection, to a self-driving car ‘seeing’ the road ahead with pedestrians, oncoming cars, or the occasional animal crossing the road. Implementing deep learning in these types of applications requires a lot of processing power with the lowest possib... » read more

Predictions: Manufacturing, Devices And Companies

Some predictions are just wishful thinking, but most of these are a lot more thoughtful. They project what needs to happen for various markets or products to become successful. Those far reaching predictions may not fully happen within 2018, but we give everyone the chance to note the progress made towards their predictions at the end of the year. (See Reflection On 2017: Design And EDA and Man... » 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 Efficiency Problem

Part one of this report addressed the efficiency problem in neural networks. This segment addresses efficiencies in training, quantization, and optimizing the network and the hardware. Minimize the Bits (CNN Advanced Quantization) Training a CNN involves assigning weight vectors to certain results, and applying adaptive filters to those results to determine the positives, false positives, a... » read more

What’s Next In Neural Networking?

Faster chips, more affordable storage, and open libraries are giving neural network new momentum, and companies are now in the process of figuring out how to optimize it across a variety of markets. The roots of neural networking stretch back to the late 1940s with Claude Shannon’s Information Theory, but until several years ago this technology made relatively slow progress. The rush towar... » read more

The Multiplier And The Singularity

In 1993, Vernor Vinge, a computer scientist and science fiction writer, first described an event called the Singularity—the point when machine intelligence matches and then surpasses human intelligence. And since then, top scientists, engineers and futurists have been asking just how far away we are from that event. In 2006, Ray Kurzweil published a book, "The Singularity is Near," in whic... » read more

Using Automotive-Ready IP To Accelerate SoC Development

IP suppliers play a key role in the automotive supply chain to enable high-performance advanced driver assistance system (ADAS) SoCs. Vision-based SoCs may contain a high amount of third-party IP to implement the key embedded vision, sensor fusion, multimedia, security and advanced connectivity functions. And while IP suppliers have permeated the semiconductor ecosystem for consumer, mobile, PC... » read more

The Next Big Threat

In just the past year, tens of millions of Target store customers had their customer and credit card records stolen, The New York Times and The Wall Street Journal were hacked, Adobe software had a security breach, Yahoo! was infected with malware, and Snapchat was hit with a bug that exposed user phone numbers. And this was just what was reported in the mainstream media. The threat, it turns o... » read more