Automatic driver assistance system (ADAS) is one of the fastest growing segments in automobile industry. It is the pioneer of innovations to make the driving experience on our busiest roads easier and safer. In recent years, ADAS functions such as radar or camera-based systems have been introduced to make driving safer. Always keep an eye on the road and warn the driver in real time of impending dangers. …


We all know that, Deep learning uses a multi-layer neural network model. It requires multi-layers to extract high-level features. These features are a combination of low level abstractions to find the distributed data features which are used to solve complex problems in ML. Deep Neural Networks (DNNs) and Convolution Neural Networks (CNNs) are the most widely used neural models for deep learning. These models are known for capability in solving picture recognition, voice recognition and other complex machine learning tasks..

That being said, as the accuracy and complexity requirements for practical applications develop, the size of neural networks increases at…


FPGAs are increasingly being used for the implementation of image processing applications. This is especially the case for real-time embedded applications, where latency and power are important considerations. An FPGA embedded in a smart camera is able to perform much of the image processing directly as the image is streamed from the sensor, with the camera providing a processed output data stream, rather than a sequence of images.

Unfortunately, simply porting a software algorithm onto an FPGA often gives disappointing results, because many image processing algorithms have been optimised for a serial processor. It is usually necessary to transform the…


FPGA(Field-Programmable Gate Array) is an array of interconnected digital sub circuits that implement common functions while also offering very high levels of flexibility. Deep learning(DL) shows excellent ability in solving complex learning problems. To give users better experience, high performance implementations of deep learning applications seem very important. An FPGA provides an extremely low-latency, flexible architecture that enables deep learning acceleration in a power-efficient solution.

There are two subsets of artificial neural networks

>Deep Neural Networks (DNNs) and

>Convolutional Neural Networks (CNNs)

Need of DL prediction process accelerator based FPGA -

The computational algorithm is called Deep Learning . The…

SANIA SHINDE

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