Deep learning has advanced rapidly, driving breakthroughs in image recognition, natural language processing, and autonomous ...

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 ...

Microchip Technology has added an HLS design workflow, called SmartHLS, to its PolarFire FPGA families to allow C++ algorithms to be directly translated to FPGA-optimised Register Transfer Level (RTL) ...

Nevertheless, an FPGA option is too expensive for high-volume applications, such as domestic appliances; for those appliances, IR will produce configurable versions of the most common algorithms in ...

The NSF has funded projects that will investigate how deep learning algorithms run on FPGAs and across systems using the high-performance RDMA interconnect. Another project, led by Andrew Ng and ...

In the last couple of years, we have written and heard about the usefulness of GPUs for deep learning training as well as, to a lesser extent, custom ASICs and FPGAs. All of these options have shown ...

We just love it that he details how to get the information you’re looking for out of this document, and shows his method of turning that first into a flow chart and then into code for the FPGA.

Today Intel announced record results on a new benchmark in deep learning and convolutional neural networks (CNN). ZTE’s engineers used Intel’s midrange Arria 10 FPGA for a cloud inferencing ...

The real beauty of this algorithm is that you can implement it with a very small FPGA footprint. CORDIC requires only a small lookup table, along with logic to perform shifts and additions.