Getting into FPGA design isn’t a monolithic experience. You have to figure out a toolchain, learn how to think in hardware during the design, and translate that into working Verliog. The end goal is ...

A wave of machine-learning-optimized chips is expected to begin shipping in the next few months, but it will take time before data centers decide whether these new accelerators are worth adopting 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 ...

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 ...

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 ...

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 ...

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 ...