Author's Latest Posts


Developing ASIL Ready SoCs For Self-Driving Cars


Artificial intelligence (AI) and deep learning using neural networks is a powerful technique for enabling advanced driver-assistance systems (ADAS) and greater autonomy in vehicles. As AI research moves rapidly, designers are facing tough competition to provide efficient, flexible, and scalable silicon and software to handle deep learning automotive applications like inferencing in embedded vis... » read more

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

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