Despite some of the inherent complexities of using FPGAs for implementing deep neural networks, there is a strong efficiency case for using reprogrammable devices for both training and inference.

Over the last couple of years, the idea that the most efficient and high performance way to accelerate deep learning training and inference is with a custom ASIC—something designed to fit the specific ...

DSP systems are best described by using a combination of both graphical-and language-based methods. The MathWorks, an industry leader in DSP modeling software, caters to this dichotomy by providing a ...

Deep learning and complex machine learning has quickly become one of the most important computationally intensive applications for a wide variety of fields. The combination of large data sets, ...

FPGAs or GPUs, that is the question. Since the popularity of using machine learning algorithms to extract and process the information from raw data, it has been a race between FPGA and GPU vendors to ...

Digi-Key have been producing YouTube videos for a number of years now, and if you weren’t aware, they’re definitely worthy of some viewing time. The playlist we’re highlighting here is a pretty good ...

DeepCoder is a machine learning system that can write its own code. It does this using a technique called program synthesis. Essentially, it creates new programs by combining existing lines of code ...