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 ...

FPGAs are used to emulate sensors and other chips for early testing. The company has worked on an owner recognition feature, ...

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 ...

Continued exponential growth of digital data of images, videos, and speech from sources such as social media and the internet-of-things is driving the need for analytics to make that data ...

Xilinx has announced that Baidu, a Chinese language Internet search provider, is utilizing Xilinx FPGAs to accelerate machine learning applications in its datacenters in China. The two companies are ...

FPGAs provide a balance of performance and flexibility required in advanced video processing applications. This white paper describes benefits of FPGAs for video streaming, content creation and AI and ...

Multi-FPGA prototyping of ASIC and SoC designs allows verification teams to achieve the highest clock rates among emulation techniques, but setting up the design for prototyping is complicated and ...

What’s the killer app for FPGAs? For some people, the allure is the ultra-high data throughput for parallelizable tasks, which can enable some pretty gnarly projects. But what if you’re just starting ...

Mipsology’s Zebra Deep Learning inference engine is designed to be fast, painless, and adaptable, outclassing CPU, GPU, and ASIC competitors. I recently attended the 2018 Xilinx Development Forum (XDF ...

A number of tools are available to help designers develop and work with FGPAS. Hymel discusses the open-source Ice40 FPGA toolchain, which includes apio, yosys, nextpnr, and Project IceStorm. He walks ...