What happens when you use DRAM at extremely low temperatures?
Rambus Chief Scientist Craig Hampel talks with Semiconductor Engineering about quantum computing and the power/performance benefits of running DRAM at extremely low temperatures.
AI-enabled systems are being designed to process more data locally as device scaling benefits decline.
Foundry forms ASIC subsidiary as it focuses on 14nm/12nm and above.
Market overcrowding, more efficient manufacturing, and growing list of scaling issues create a challenging competitive landscape.
Growing volume of data and limited improvements in performance create new opportunities for approaches that never got off the ground.
Old solutions don’t necessarily work anymore, particularly at advanced nodes and ultra-low voltage.
Costs of developing a complex chip could run as high as $1.5B, while power/performance benefits are likely to decrease.
AI-enabled systems are being designed to process more data locally as device scaling benefits decline.
Lines blur as processors are added into traditional FPGAs, and programmability is added into ASICs.
Foundry forms ASIC subsidiary as it focuses on 14nm/12nm and above.
Technology has the potential to reshape processing everywhere, starting with limited scientific and commercial applications.
Market overcrowding, more efficient manufacturing, and growing list of scaling issues create a challenging competitive landscape.
Using the Von Neumann architecture for artificial intelligence applications is inefficient. What will replace it?
Experts at the Table, part 1: Why machine learning works in some cases and not in others.
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Big thank you for this interview, Ed. When I first heard about this, I found it counter-intuitive. Surely electrons would slow down. How could it work? Loved the interview and your questions. Very “cool” stuff in more ways than K.