Les 10 concurrents opencompute.org les plus visités

penguincomputing.com

Penguin Solutions designs, builds, deploys, and manages large, complex Al and high-performance computing (HPC) infrastructures at scale.

amax.com

General The AMAX Group, comprising AMAX Engineering Corporation in the USA and its subsidiaries AMAX Information Technologies Limited in Ireland, AMAX Information Technologies (Suzhou) CO.,LTD, AMAX Information Technologies (Dongguan) CO.,LTD and AMAX Information Technology (Shanghai) CO.,LTD in China (collectively referred to as “AMAX”) respects your privacy and

snia.org

SNIA is an industry organization that develops global standards and delivers education on all technologies related to data. We are the experts on data.

servethehome.com

We review the cheap 10GbE NICs, the Realtek RTL8127, bringing 10Gbase-T to small, low-power, PCIe Gen4 x1 NICs

supermicro.com

Warranty information such as limited warranty, warranty void conditions, product returns, RMA requirements, and cancellations can be found here.

fpga.org

Exploring Parallel Computer Architecture with FPGAs

chipsalliance.org

CHIPS Alliance’s latest Deep Dive Cafe featured an outstanding talk by a Google physical design engineer, Young-Joon Lee, who has a PhD from Georgia Tech, and has been working on machine learning physical design projects for the past two years. The chip placement problem is a notoriously challenging design problem, and has been explored in the electronic design automation research and development community for years. For those unfamiliar with the problem, it involves finding the optimal placement of physical cells implementing the logical function on a chip image to minimize performance, power, and area of the silicon, which in the end affects the cost of the product. The effort by Google to apply machine learning to the placement problem started as part of the Google Brain effort, which in part focuses on running algorithms at scale on large amounts of data.