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 ...

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 ...

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 ...

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 ...

This afternoon Microsoft announced Brainwave, an FPGA-based system for ultra-low latency deep learning in the cloud. Early benchmarking indicates that when using Intel Stratix 10 FPGAs, Brainwave can ...

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 ...

FPGAs continue to gain ground in the edge AI arena thanks to their combination of reconfigurable hardware and deterministic, low-latency performance.

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 ...

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 ...

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 ...