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 ...

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 ...

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.

Field-Programmable Gate Arrays (FPGAs) have emerged as a versatile platform for accelerating computer vision tasks by exploiting fine-grained parallelism, deep pipelining and reconfigurable logic.

Flex Logix Technologies, Inc. announced today that its embedded FPGA will be integrated into a next-generation deep learning chip in TSMC 16FFC that is being developed by the research group of ...

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 ...

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 ...

Developers and AI enthusiasts may be interested in a new piece of kit and service unveiled by Innodisk this week in the form of the FPGA Machine Vision Platform. Powered by AMD’s Xilinx Kria K26 SOM ...

CHANDLER, Ariz., July 15, 2019 /PRNewswire/ -- As compute-intensive, vision-based systems are increasingly integrated at the network edge, Field Programmable Gate Arrays (FPGAs) are quickly becoming a ...