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 ...
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 ...
Microsoft is set to capitalize on its work on integrating field programmable gate arrays (FPGAs) into servers for machine learning workloads, by launching a specialized Azure cloud service. Project ...
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 ...
In the last couple of years, we have written and heard about the usefulness of GPUs for deep learning training as well as, to a lesser extent, custom ASICs and FPGAs. All of these options have shown ...
Today Intel announced record results on a new benchmark in deep learning and convolutional neural networks (CNN). Developed with ZTE, a leading technology telecommunications equipment and systems ...
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 ...
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 ...
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 ...
Artificial intelligence (AI) originated in classical philosophy and has been loitering in computing circles for decades. Twenty years ago, AI surged in popularity, but interest waned as technology ...