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 ...

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 ...

Affective computing, a field focused on understanding and emulating human emotions, has seen significant advancements thanks ...

It is no secret that we like the Lattice iCE40 FPGA. It has a cheap development board and an open source toolchain, so it is an easy way to get started developing low-cost, low-power FPGA designs.

This November, enroll in a free or paid training course to take your AI knowledge to the next level. Check out our list of ...

In the rapidly advancing field of computational biology, a review explores the transformative role of deep learning ...

Recently, a research team has collaborated to propose a deep learning model for predicting protein conformational changes.

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 ...

In addition to the market challenges, most startups also had a flawed business strategy. Most AI startups had a similar ...

Formed from Intel’s $16.7 billion acquisition of Altera in 2015, PSG develops field programmable gate arrays (FPGAs), complex programmable ... such as the Gaudi deep learning processors and ...

His current research interests include deep learning/artificial neural networks, adaptive/nonlinear signal processing, digital signal processing, multimedia (speech, video) and VLSI/DSP/FPGA ...

FPGAs have become an increasingly popular approach ... next year at turning its losses to break-even. The lowdown on deep learning: from how it relates to the wider field of machine learning ...