tag: machine learning

Automated Optical Inspection
By Ed Sperling - 07 Nov, 2022 - Comments: 0

Building good automated models for inspection require more data to be collected, both good and bad. Vijay Thangamariappan, R&D engineer at Advantest, explains how to develop models for automating optical inspection, using a multi-thousand pin socket as an example for how machine learning has helped reduce the return rate due to defects from 2% down to zero. He also explains how to achieve t... » read more

Complex Tradeoffs In Inferencing Chips
By John Koon - 17 Oct, 2022 - Comments: 0

Designing AI/ML inferencing chips is emerging as a huge challenge due to the variety of applications and the highly specific power and performance needs for each of them. Put simply, one size does not fit all, and not all applications can afford a custom design. For example, in retail store tracking, it's acceptable to have a 5% or 10% margin of error for customers passing by a certain aisle... » read more

Training a ML model On An Intelligent Edge Device Using Less Than 256KB Memory
By Technical Paper Link - 11 Oct, 2022 - Comments: 0

A new technical paper titled "On-Device Training Under 256KB Memory" was published by researchers at MIT and MIT-IBM Watson AI Lab. “Our study enables IoT devices to not only perform inference but also continuously update the AI models to newly collected data, paving the way for lifelong on-device learning. The low resource utilization makes deep learning more accessible and can have a bro... » read more

More Efficient Matrix-Multiplication Algorithms with Reinforcement Learning (DeepMind)
By Technical Paper Link - 11 Oct, 2022 - Comments: 0

A new research paper titled "Discovering faster matrix multiplication algorithms with reinforcement learning" was published by researchers at DeepMind. "Here we report a deep reinforcement learning approach based on AlphaZero for discovering efficient and provably correct algorithms for the multiplication of arbitrary matrices," states the paper. Find the technical paper link here. Publis... » read more

Simplifying AI Edge Deployment
By Ed Sperling - 10 Oct, 2022 - Comments: 0

Barrie Mullins, vice president of product at Flex Logix, explains how a programmable accelerator chip can simplify semiconductor design at the edge, where chips need to be high performance as well as low power, yet developing everything from scratch is too expensive and time-consuming. Programmability allows these systems to stay current with changes in algorithms, which can affect everything f... » read more

Speeding-Up Thermal Simulations Of Chips With ML
By Technical Paper Link - 02 Oct, 2022 - Comments: 0

A new technical paper titled "A Thermal Machine Learning Solver For Chip Simulation" was published by researchers at Ansys. Abstract "Thermal analysis provides deeper insights into electronic chips' behavior under different temperature scenarios and enables faster design exploration. However, obtaining detailed and accurate thermal profile on chip is very time-consuming using FEM or CFD. Th... » read more

Can ML Help Verification? Maybe
By Brian Bailey - 22 Sep, 2022 - Comments: 0

Functional verification produces an enormous amount of data that could be used to train a machine learning system, but it's not always clear which data is useful or whether it can help. The challenge with ML is understanding when and where to use it, and how to integrate it with other tools and approaches. With a big enough hammer, it is tempting to call everything a nail, and just throwing ... » read more

Recipe To Catch Bugs Faster Using Machine Learning
By Anika Sunda - 19 Sep, 2022 - Comments: 0

We all agree that verification and debug take up a significant amount of time and are arguably the most challenging parts of chip development. Simulator performance has consistently topped the charts and is a critical component in the verification process. Still, the need of the hour is to stretch beyond simulator speed to achieve maximum verification throughput and efficiency. Artificial in... » read more

Rethinking Machine Learning For Power
By Brian Bailey - 13 Sep, 2022 - Comments: 0

The power consumed by machine learning is exploding, and while advances are being made in reducing the power consumed by them, model sizes and training sets are increasing even faster. Even with the introduction of fabrication technology advances, specialized architectures, and the application of optimization techniques, the trend is disturbing. Couple that with the explosion in edge devices... » read more

New Data Management Challenges
By Ann Mutschler - 13 Sep, 2022 - Comments: 0

An explosion in semiconductor design and manufacturing data, and the expanding use of chips in safety-critical and mission-critical applications, is prompting chipmakers to collect and manage that data more effectively in order to improve overall performance and reliability. This collection of data reveals a number of challenges with no simple solutions. Data may be siloed and inconsistent, ... » read more

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