Field programmable gate arrays (FPGAs) have emerged as flexible hardware platforms for accelerating deep learning networks, offering high energy efficiency, low latency and reconfigurable parallelism.

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 ...

There has been much written about the potential for FPGAs to take a leadership role in accelerating deep learning but in practice, the hurdles of getting from concept to high performance hardware ...

FPGAs have long been used in the early stages of any new digital technology, given their utility for prototyping and rapid evolution. But with machine learning, FPGAs are showing benefits beyond those ...

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 ...

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 ...

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 ...

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 ...