tag: machine learning

Part Average Tests For Auto ICs Not Good Enough
By Anne Meixner - 24 Feb, 2021 - Comments: 0

Part Average Testing (PAT) has long been used in automotive. For some semiconductor technologies it remains viable, while for others it is no longer good enough. Automakers are bracing for chips developed at advanced process nodes with much trepidation. Tight control of their supply chains and a reliance upon mature electronic processes so far have enabled them to increase electronic compone... » read more

Hidden Costs In Faster, Low-Power AI Systems
By Ed Sperling - 20 Jan, 2021 - Comments: 1

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

Learning properties of ordered and disordered materials from multi-fidelity data
By Technical Paper Link - 14 Jan, 2021 - Comments: 0

Source: Chen, C., Zuo, Y., Ye, W. et al. Learning properties of ordered and disordered materials from multi-fidelity data. Nat Comput Sci 1, 46–53 (2021). https://doi.org/10.1038/s43588-020-00002-x Abstract: "Predicting the properties of a material from the arrangement of its atoms is a fundamental goal in materials science. While machine learning has emerged in recent years as a n... » read more

Machine Learning — Everywhere: Enabling Self-Optimizing Design Platforms for Better End-to-End Results
By Synopsys - 13 Jan, 2021 - Comments: 0

Machine-learning offers opportunities to enable self-optimizing design tools. Very much like self-driving cars that observe real-world interactions to improve their responses in different (local) driving conditions, AI-enhanced tools are able to learn and improve in (local) design environments after deployment. These new, ML-driven capabilities can be embedded in different design engines, gi... » read more

Machine Learning Approach for Fast Electromigration Aware Aging Prediction in Incremental Design of Large Scale On-Chip Power Grid Network
By Technical Paper Link - 31 Dec, 2020 - Comments: 0

Abstract "With the advancement of technology nodes, Electromigration (EM) signoff has become increasingly difficult, which requires a considerable amount of time for an incremental change in the power grid (PG) network design in a chip. The traditional Black’s empirical equation and Blech’s criterion are still used for EM assessment, which is a time-consuming process. In this article, for ... » read more

Top Tech Videos Of 2020
By Linda Christensen - 30 Dec, 2020 - Comments: 0

2020 shaped up to be a year of major upheaval, emerging markets and even increased demand in certain sectors. So it's not surprising that videos focusing on AI, balancing power and performance, designing and manufacturing at advanced nodes, advanced packaging, and automotive-related subjects were the most popular. Of the 68 videos published this year, the following were the most viewed in ea... » read more

5 Predictions For AI Innovation In 2021
By Arun Venkatachar - 22 Dec, 2020 - Comments: 0

By Arun Venkatachar and Stelios Diamantidis Artificial intelligence (AI) has emerged as one of the most important watchwords in all of technology. The once-utopian vision of developing machines that can think and behave like humans is becoming more of a reality as engineering innovations enable the performance required to process and interpret previously unimaginable amounts of data efficien... » read more

A Collaborative Data Model For AI/ML In EDA
By Si2 - 21 Dec, 2020 - Comments: 0

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
By Brian Bailey - 14 Dec, 2020 - Comments: 1

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

Low Power Still Leads, But Energy Emerges As Future Focus
By Ann Mutschler - 10 Dec, 2020 - Comments: 1

In 2021 and beyond, chips used in smartphones, digital appliances, and nearly all major applications will need to go on a diet. As the amount of data being generated continues to swell, more processors are being added everywhere to sift through that data to determine what's useful, what isn't, and how to distribute it. All of that uses power, and not all of it is being done as efficiently as... » read more

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