For machine learning applications, that means primarily using FPGAs for inference, rather than training. The rationale here is pretty straightforward: inference requires lower precision and less ...

Achronix’s Speedcore Gen 4 can be tailored for machine-learning applications as well as to deliver high-performance FPGA connectivity for embedded FPGAs. 1. Processor performance is starting to ...

IntroductionArtificial 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 ...

Why does SnowBell offer such great potential for companies with machine-learning applications? SnowBell is the first FPGA software that enables users to develop their machine learning application once ...

SoC bandwidth, integration expand as data centers use more FPGAs for machine learning. September 24th, 2018 - By: Kevin Fogarty A wave of machine-learning-optimized chips is expected to begin shipping ...

In this video from ATPESC 2019, James Moawad and Greg Nash from Intel present: FPGAs and Machine Learning.. Neural networks are inspired by biological systems, in particular the human brain. Through ...

Technologies often start out in one place and then find themselves in another. The architecture of the GPU was initially driven by the need for 3D graphics for video games, was co-opted as a massively ...

The current state of Artificial Intelligence (AI), in general, and Deep Learning (DL) in specific, is more tightly tying hardware to software than at any time in computers since the 1970s.

Compared to the latest FPGAs and microprocessors, AI engines improve the performance of machine learning algorithms by 20X and 100X respectively, consuming only 50% of the power. Compared to the ...