tag: machine learning

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

Modeling Effects Of Fluctuation Sources On Electrical Characteristics Of GAA Si NS MOSFETs Using ANN-Based ML
By Technical Paper Link - 07 Sep, 2022 - Comments: 0

Researchers from National Yang Ming Chiao Tung University (Taiwan) published a technical paper titled "A Machine Learning Approach to Modeling Intrinsic Parameter Fluctuation of Gate-All-Around Si Nanosheet MOSFETs." "This study has comprehensively analyzed the potential of the ANN-based ML strategy in modeling the effect of fluctuation sources on electrical characteristics of GAA Si NS MOSF... » read more

Deep Learning To Classify And Establish Structure Property Predictions With PeakForce QNM Atomic Force Microscopy
By Bruker - 07 Sep, 2022 - Comments: 0

Machine learning and specifically, deep learning, is a powerful tool to establish the presence (or absence) of microstructure correlations to bulk properties with its ability to flesh out relationships and trends that are difficult to establish otherwise. This application note discusses the use of deep learning tools, to explore AFM phase and PeakForce Quantitative Nanomechanics (QNM) im... » read more

Methods To Overcome Limited Labeled Data Sets In Machine Learning-Based Optical Critical Dimension Metrology
By Onto Innovation - 07 Sep, 2022 - Comments: 0

With the aggressive scaling of semiconductor devices, the increasing complexity of device structure coupled with tighter metrology error budget has driven up Optical Critical Dimension (OCD) time to solution to a critical point. Machine Learning (ML), thanks to its extremely fast turnaround, has been successfully applied in OCD metrology as an alternative solution to the conventional physical... » read more

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