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Putting Limits On What AI Systems Can Do


New techniques and approaches are starting to be applied to AI and machine learning to ensure they function within acceptable parameters, only doing what they're supposed to do. Getting AI/ML/DL systems to work has been one of the biggest leaps in technology in recent years, but understanding how to control and optimize them as they adapt isn't nearly as far along. These systems are generall... » read more

Final Report: National Security Commission on AI


  In August 2018, Section 1051 of the John S. McCain National Defense Authorization Act for Fiscal Year 2019 established the National Security Commission on Artificial Intelligence as an independent Commission “to consider the methods and means necessary to advance the development of artificial intelligence, machine learning, and associated technologies to comprehensively address the... » read more

AI In Inspection, Metrology, And Test


AI/ML is creeping into multiple processes within the fab and packaging houses, although not necessarily for the purpose it was originally intended. The chip industry is just beginning to learn where AI makes sense and where it doesn't. In general, AI works best as a tool in the hands of someone with deep domain expertise. AI can do certain things well, particularly when it comes to pattern m... » read more

New Uses For AI


AI is being embedded into an increasing number of technologies that are commonly found inside most chips, and initial results show dramatic improvements in both power and performance. Unlike high-profile AI implementations, such as self-driving cars or natural language processing, much of this work flies well under the radar for most people. It generally takes the path of least disruption, b... » read more

Making Sure AI/ML Works In Test Systems


Artificial intelligence/machine learning is being utilized increasingly to find patterns and outlier data in chip manufacturing and test, improving the overall yield and reliability of end devices. But there are too many variables and unknowns to reliably predict how a chip will behave in the field using just AI. Today, every AI use case — whether a self-driving car or an industrial sortin... » read more

The Chip Industry’s Next-Gen Roadmap


Todd Younkin, the new president and chief executive of the Semiconductor Research Corp. (SRC), sat down with Semiconductor Engineering to talk about engineering careers, R&D trends and what’s ahead for chip technologies over the next decade. What follows are excerpts of that conversation. SE: As a U.S.-based chip consortium, what is SRC's charter? Younkin: The Semiconductor Research... » read more

Hidden Costs In Faster, Low-Power AI Systems


Chipmakers are building orders of magnitude better performance and energy efficiency into smart devices, but to achieve those goals they also are making tradeoffs that will have far-reaching, long-lasting, and in some cases unknown impacts. Much of this activity is a direct result of pushing intelligence out to the edge, where it is needed to process, sort, and manage massive increases in da... » read more

Why AI Systems Are So Hard To Predict


AI can do many things, but how to ensure that it does the right things is anything but clear. Much of this stems from the fact that AI/ML/DL systems are built to adapt and self-optimize. With properly adjusted weights, training algorithms can be used to make sure these systems don't stray too far from the starting point. But how to test for that, in the lab, the fab and in the field is far f... » read more

A Collaborative Data Model For AI/ML In EDA


This work explores industry perspectives on: Machine Learning and IC Design Demand for Data Structure of a Data Model A Unified Data Model: Digital and Analog examples Definition and Characteristics of Derived Data for ML Applications Need for IP Protection Unique Requirements for Inferencing Models Key Analysis Domains Conclusions and Proposed Future Work Abstra... » read more

Power Models For Machine Learning


AI and machine learning are being designed into just about everything, but the chip industry lacks sufficient tools to gauge how much power and energy an algorithm is using when it runs on a particular hardware platform. The missing information is a serious limiter for energy-sensitive devices. As the old maxim goes, you can't optimize what you can't measure. Today, the focus is on functiona... » read more

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