Princeton researchers have totally reimagined the physics of computing to build a chip for modern AI workloads, and with new U.S. government backing they will see how fast, compact and power-efficient this chip can get. An early prototype is pictured above. Credit: Hongyang Jia/Princeton Universityand EnCharge AI, promises significant improvements in energy efficiency and computing power, aiming to revolutionize AI’s accessibility and application.
“There’s a pretty important limitation with the best AI available just being in the data center,” Verma said. “You unlock it from that and the ways in which we can get value from AI, I think, explode.” Encharge AI “brings leadership in the development and execution of robust and scalable mixed-signal computing architectures,” according to the project proposal. Vermain 2022 with Kailash Gopalakrishnan, a former IBM Fellow, and Echere Iroaga, a leader in semiconductor systems design.
When Nvidia became only the third company ever to reach a $2 trillion valuation, the Wall Street Journalthat a rapidly increasing share of the company’s rising revenue came not through the development of the models, called training, but in chips that enable the use of AI systems once they are already trained. Technologists refer to this deployment stage as inference. And inference is where Verma says his research will have the most impact in the near-to-medium term.
But so far, digital approaches to in-memory computing have been highly limited. Verma and his team turned to an alternate approach: analog computation. That’s part two.
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