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MIT Makes a Big Breakthrough in Nonsilicon Transistors
December 10, 2020
What if Silicon Valley moved beyond silicon? In the 80’s, Seymour Cray was asking the same question, delivering at Supercomputing 1988 a talk titled “What’s All This About Gallium Arsenide?” The supercomputing legend intended to make gallium arsenide (GaA) the material of the future... Read more…
Discovering Alternative Solar Panel Materials with Supercomputing
May 23, 2020
Solar power is quickly growing in the world’s energy mix, but silicon – a crucial material in the construction of photovoltaic solar panels – remains expe Read more…
MIT Researchers Build Carbon Nanotube Microprocessor
September 5, 2019
A years-long mission to build a microprocessor out of carbon nanotube transistors has finally succeeded thanks to a team of MIT researchers. The development comes as the sustainability of Moore’s Law is increasingly called into question. Silicon-based transistors are nearing the point when they will be unable to shrink anymore, delivering increasingly marginal improvements. Read more…
Researchers Advance Graphene’s Potential as Silicon Alternative
March 30, 2017
In the face of a slowing Moore's law for silicon-based CMOS technology, researchers are on the hunt for a successor to silicon. One of the more promising candid Read more…
Purdue Showcases New Concepts in Semiconducting
December 15, 2016
At the international IEDM 2016 conference earlier this month, Purdue University researchers revealed a number of technologies and concepts aimed at transformin Read more…
Report Addresses the Perils of Dark Silicon
July 21, 2016
Dark silicon refers to the processing potential that's lost when thermal constraints disallow full CPU utilization. The gap between transistor scaling and voltage scaling combined with tighter integration of components (multicore, SoCs) has power density ramifications that are of particular concern for embedded computing, but high-performance computing faces similar "dark power" challenges. Bringing attention to this issue and exploring common solutions was the goal of the Dagstuhl Seminar 16052, “Dark Silicon: From Embedded to HPC Systems.” Read more…
LBNL Researchers Report Method to Make “Atomically Thin” Circuits
July 14, 2016
This week researchers from Lawrence Berkeley National Laboratory have reported a new method to create transistors and circuits that are only a few atoms thick. Read more…
RISC-V Startup Aims to Democratize Custom Silicon
July 13, 2016
Momentum for open source hardware made a significant advance this week with the launch of startup SiFive and its open source chip platforms based on the RISC-V instruction set architecture. The founders of the fabless semiconductor company — Krste Asanovic, Andrew Waterman, and Yunsup Lee — invented the free and open RISC-V ISA at the University of California, Berkeley, six years ago. The progression of RISC-V and the launch of SiFive opens the door to a new way of chip building that skirts prohibitive licensing costs and lowers the barrier to entry... Read more…
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Whitepaper
A New Standard in CAE Solutions for Manufacturing
Today, manufacturers of all sizes face many challenges. Not only do they need to deliver complex products quickly, they must do so with limited resources while continuously innovating and improving product quality. With the use of computer-aided engineering (CAE), engineers can design and test ideas for new products without having to physically build many expensive prototypes. This helps lower costs, enhance productivity, improve quality, and reduce time to market.
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Whitepaper
Porting CUDA Applications to Run on AMD GPUs
A workload-driven system capable of running HPC/AI workloads is more important than ever. Organizations face many challenges when building a system capable of running HPC and AI workloads. There are also many complexities in system design and integration. Building a workload driven solution requires expertise and domain knowledge that organizational staff may not possess.
This paper describes how Quanta Cloud Technology (QCT), a long-time Intel® partner, developed the Taiwania 2 and Taiwania 3 supercomputers to meet the research needs of the Taiwan’s academic, industrial, and enterprise users. The Taiwan National Center for High-Performance Computing (NCHC) selected QCT for their expertise in building HPC/AI supercomputers and providing worldwide end-to-end support for solutions from system design, through integration, benchmarking and installation for end users and system integrators to ensure customer success.
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