Chip Industry Technical Paper Roundup: June 10


New technical papers added to Semiconductor Engineering’s library this week. [table id=232 /] More ReadingTechnical Paper Library home » read more

CAM-Based CMOS Implementation Of Reference Frames For Neuromorphic Processors (Carnegie Mellon U.)


A technical paper titled “NeRTCAM: CAM-Based CMOS Implementation of Reference Frames for Neuromorphic Processors” was published by researchers at Carnegie Mellon University. Abstract: "Neuromorphic architectures mimicking biological neural networks have been proposed as a much more efficient alternative to conventional von Neumann architectures for the exploding compute demands of AI work... » read more

Chip Industry Week In Review


Applied Materials may scale back or cancel its $4 billion new Silicon Valley R&D facility in light of the U.S. government's recent announcement to reduce funding for construction, modernization, or expansion of semiconductor research and development (R&D) facilities in the United States, according to the San Francisco Chronicle. TSMC could receive up to $6.6 billion in direct funding... » read more

Chip Industry Technical Paper Roundup: March 26


New technical papers recently added to Semiconductor Engineering’s library. [table id=209 /] Find last week's technical paper additions here. » read more

TCAM-SSD: A Framework For In-SSD Associative Search Using NAND Flash Memory


A new technical paper titled "TCAM-SSD: A Framework for Search-Based Computing in Solid-State Drives" was published by researchers at University of Illinois Urbana-Champaign, Carnegie Mellon University, Samsung Electronics and Sandia National Laboratories. Abstract "As the amount of data produced in society continues to grow at an exponential rate, modern applications are incurring signific... » read more

Chip Industry Technical Paper Roundup: Mar. 11


New technical papers added to Semiconductor Engineering’s library this week. [table id=205 /] More ReadingTechnical Paper Library home » read more

Efficient Streaming Language Models With Attention Sinks (MIT, Meta, CMU, NVIDIA)


A technical paper titled “Efficient Streaming Language Models with Attention Sinks” was published by researchers at Massachusetts Institute of Technology (MIT), Meta AI, Carnegie Mellon University (CMU), and NVIDIA. Abstract: "Deploying Large Language Models (LLMs) in streaming applications such as multi-round dialogue, where long interactions are expected, is urgently needed but poses tw... » read more

Latency, Interconnects, And Poker


Semiconductor Engineering sat down with Larry Pileggi, Coraluppi Head and Tanoto Professor of Electrical and Computer Engineering at Carnegie Mellon University, and the winner of this year's Phil Kaufman Award for Pioneering Contributions. What follows are excerpts of that conversation. SE: When did you first get started working in semiconductors — and particularly, EDA? Pileggi: This w... » read more

Chip Industry Talent Shortage Drives Academic Partnerships


Universities around the world are forming partnerships with semiconductor companies and governments to help fill open and future positions, to keep curricula current and relevant, and to update and expand skills for working engineers. Talent shortages repeatedly have been cited as the number one challenge for the chip industry. Behind those concerns are several key drivers, and many more dom... » read more

Technical Paper Roundup: Sept 11


New technical papers added to Semiconductor Engineering’s library this week. [table id=136 /] (more…) » read more

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