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 ...

Four choices to accelerate deep learning inference Over time ... central processing units (CPUs), graphics processing units (GPUs), FPGAs, and application-specific integrated circuits (ASICs). The ...

A technical paper titled “Application of Machine Learning in FPGA EDA Tool Development” was published by researchers at the University of Texas Dallas. “With the recent advances in hardware ...

These tasks include scientific simulations, data analytics, and machine learning. HPC plays a critical role in various industries, such as finance, healthcare, and oil and gas exploration. Industry ...

A new technical paper titled “Hacking the Fabric: Targeting Partial Reconfiguration for Fault Injection in FPGA Fabrics” was ...

The two major hardware choices for running AI applications are FPGAs and GPUs. Although GPUs can handle the massive volumes of data necessary for AI and deep learning, they have limitations regarding ...

The new update enables real-time machine learning for signal analysis and more, using modular platform tools for research applications.

This ebook covers IoT, machine vision, and AI, comparing FPGAs, adaptive SoCs, and ASICs. It focuses on cost-optimized FPGAs, offering software flexibility and hardware efficiency to help ...

Altera, a division of Intel that makes programmable chips, unveiled today a number of products that will bring more AI to the edge and the cloud.

Discover how adaptive-computing technology is extending the capabilities of LiDAR sensors to deliver higher depth resolution ...

Achronix Semiconductor, a leader in high-performance FPGAs and embedded FPGA (eFPGA) IP, today announced it will host a LinkedIn Live Webinar in collaboration with Primemas and Blue Cheetah, and ...