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 ...

Hardware and device makers are in a mad dash to create or acquire the perfect chip for performing deep learning training and inference. While we have yet to see anything that can handle both parts of ...

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 ...

Intel and ZTE, a leading technology telecommunications equipment and systems company, have worked together to reach a new benchmark in deep learning and convolutional neural networks (CNN). The ...

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 ...

Applications and infrastructure evolve in lock-step. That point has been amply made, and since this is the AI regeneration era, infrastructure is both enabling AI applications to make sense of the ...

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 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 ...