September 22, 2022
Microsoft shared details on how it uses an AMD technology to secure artificial intelligence as it builds out a secure AI infrastructure in its Azure cloud service. Microsoft has a strong relationship with Nvidia, but is also working with AMD's Epyc chips (including the new 3D VCache series), MI Instinct accelerators, and also... Read more…
September 17, 2022
The need for speed is a hot topic among participants at this week’s AI Hardware Summit – larger AI language models, faster chips and more bandwidth for AI machines to make accurate predictions. But some hardware startups are taking a throwback approach for AI computing to counter the more-is-better... Read more…
September 14, 2022
When DeepMind, an Alphabet subsidiary, started off more than a decade ago, solving some most pressing research questions and problems with AI wasn’t at the top of the company’s mind. Instead, the company started off AI research with computer games. Every score and win was a measuring stick of success... Read more…
September 14, 2022
Chipmaker Cerebras is patching its chips – already considered the world's largest – to create what could be the largest-ever computing cluster for AI computing. A reasonably sized "wafer-scale cluster," as Cerebras calls it, can network together 16 CS-2s into a cluster to create a computing system with 13.6 million cores for natural... Read more…
September 14, 2022
SambaNova Systems is announcing – and shipping – its second-generation DataScale system, the DataScale SN30. Powered by the eponymous Cardinal SN30 RDU (Rec Read more…
August 19, 2022
Next month the AI Hardware Summit returns to the Bay Area, bringing AI technologists and end users together to share ideas and get up to speed on all the latest AI hardware developments. The event – which takes place September 13-15, 2022, at the Santa Clara Marriott, Calif. – will be co-located with the Edge AI Summit. Both events are organized by... Read more…
September 15, 2021
What will system memory look like in five years? Good question. While Monday's panel, Designing AI Super-Chips at the Speed of Memory, at the AI Hardware Summit, tackled several topics, the panelists also took a brief glimpse into the future. Unlike compute, storage and networking, which... Read more…
October 13, 2020
AI chip and systems startup Cerebras was one of many AI companies showcased at the AI Hardware Summit which concluded last week. Rather than dwell on its techno Read more…
Data centers are experiencing increasing power consumption, space constraints and cooling demands due to the unprecedented computing power required by today’s chips and servers. HVAC cooling systems consume approximately 40% of a data center’s electricity. These systems traditionally use air conditioning, air handling and fans to cool the data center facility and IT equipment, ultimately resulting in high energy consumption and high carbon emissions. Data centers are moving to direct liquid cooled (DLC) systems to improve cooling efficiency thus lowering their PUE, operating expenses (OPEX) and carbon footprint.
This paper describes how CoolIT Systems (CoolIT) meets the need for improved energy efficiency in data centers and includes case studies that show how CoolIT’s DLC solutions improve energy efficiency, increase rack density, lower OPEX, and enable sustainability programs. CoolIT is the global market and innovation leader in scalable DLC solutions for the world’s most demanding computing environments. CoolIT’s end-to-end solutions meet the rising demand in cooling and the rising demand for energy efficiency.
Divergent Technologies developed a digital production system that can revolutionize automotive and industrial scale manufacturing. Divergent uses new manufacturing solutions and their Divergent Adaptive Production System (DAPS™) software to make vehicle manufacturing more efficient, less costly and decrease manufacturing waste by replacing existing design and production processes.
Divergent initially used on-premises workstations to run HPC simulations but faced challenges because their workstations could not achieve fast enough simulation times. Divergent also needed to free staff from managing the HPC system, CAE integration and IT update tasks.
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