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May 2021

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Webinar by Prof. Biswajit Ray, U Alabama on "3D NAND based Storage Systems for Hardware-security, Reliability and Edge-computing"
by Gupta, Sumeet Kumar 31 May '21

31 May '21

Hello It is my great pleasure to host Prof. Biswajit Ray, University of Alabama for his talk in the ECE Seminar Series. The details are below and attached. Hope to see you in the seminar. Many thanks to Prof. Alam for his kind suggestion. Thanks Sumeet When: June 3, 2021 (Thursday), 9:30 AM – 10:30 AM (ET) Where: https://purdue-edu.zoom.us/j/3164232249 Title: 3D NAND based Storage Systems for Hardware-security, Reliability and Edge-computing Abstract: Non-volatile memory such as NAND flash technology is ubiquitous in electronic systems that are used in many unmanned autonomous vehicles, sensor nodes, space and defense electronics and internet of things applications. While NAND flash memory offers high-density, low-cost, and light-weight storage solutions in small form factors, they suffer from security vulnerabilities and reliability issues. Even though emerging non-volatile memories offer high security and reliability, they are limited in capacity (in Megabytes) and cost, making them impractical for many emerging data-intensive applications, such as artificial intelligence, machine learning, and predictive analytics. Thus, there exists a need to enhance the security and reliability of the commercial-off-the-shelf (COTS) NAND flash memory to enable data-intensive applications at the edge nodes and extreme environments. In this talk, I will share my experimental research on the security, reliability and low-power computing using COTS 3D NAND flash memory. I will introduce a unique device-system interactive framework for 3D NAND system which enables creation of hardware-security functions such as, PUF, TRNG, Watermark, true-sanitization and different anti-counterfeit solutions. In addition, I will illustrate the application of 3D NAND for the future low-power computing systems. Finally, I will present a conceptual framework to understand the radiation effects on the 3D NAND storage and system-level error-mitigation strategies. Speaker Bio: Biswajit Ray is an Assistant Professor of Electrical and Computer Engineering with the University of Alabama in Huntsville, AL, USA, where he leads Hardware Reliability Laboratory. Dr. Ray received Ph.D. from Purdue University, West Lafayette, IN and then he worked in SanDisk Corporation, Milpitas, California developing 3D NAND Flash memory technology. Dr. Ray holds 15 U.S. issued patents on non-volatile memory systems, published more than 50 research papers in international journals and conferences. Dr. Ray is a senior member, IEEE and the recipient of Outstanding Faculty Research Award (2020), The University of Alabama in Huntsville.

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Still time to register to today’s and next week's hands-on machine learning workshop using nanoHUB
by Turner, Jaime J 26 May '21

26 May '21

Dear Colleagues, A few available seats remain for the first two workshops in the Summer 2021 session of our Hands-on Data Science and Machine Learning Training Series. Series information. Our series is aimed at active researchers and educators and designed to introduce practical skills with online, hands-on activities participnts will be able to incorporate in their work. Hands-on activities will use nanoHUB cloud computing resources, no need to download or install any software. All you need is an internet connection and a browser. After the training sessions, you will be able to continue using nanoHUB for research or education. Registration links and material for prior workshops can be found at the workshop webpage: https://nanohub.org/groups/ml/handsontraining Register soon as seats are limited. Title: A Hands-on Introduction to Physics-Informed Neural Networks Date: May 26th 2021, 1:30 PM - 2:30 PM EST Speaker: Ilias Bilionis, Purdue University Register here (limited seats): https://nanohub.org/groups/ml/handsontraining Abstract: Can you make a neural network satisfy a physical law? There are two main types of these laws: symmetries and ordinary/partial differential equations. I will focus on differential equations in this short presentation. The simplest way to bake information about a differential equation with neural networks is to create a regularization term for the loss function used in training. I will explain the mathematics of this idea. I will also talk about applying physics-informed neural networks to a plethora of applications spanning the range from solving differential equations for all possible parameters in one sweep (e.g., solve for all boundary conditions) to calibrating differential equations using data to design optimization. Then, we will work on a hands-on activity that shows you to implement the ideas in PyTorch. I am assuming some familiarity with how conventional neural networks are trained (stochastic gradient descent). Also, you need to know the basics of PyTorch to follow along. Going over this tutorial should be sufficient: https://pytorch.org/tutorials/beginner/pytorch_with_examples.html. Title: Batch Reification Fusion Optimization (BAREFOOT) Framework Date: June 2nd 2021, 1:30 PM - 2:30 PM EST Speaker: Richard Couperthwaite, Texas A&M University Register here (limited seats): https://nanohub.org/groups/ml/handsontraining Abstract: This tutorial will present the fundamentals of multi-fidelity fusion as well as Sequential and Batch Bayesian Optimization as possible optimization approaches that can be integrated with high accuracy computational models or experimental procedures to speed up the optimization or design of materials. The tutorial will also provide an overview of the BAREFOOT framework that combines these two approaches together to further improve the rate of the optimization. All these approaches are compared using the optimization of the workability of a dual-phase steel microstructure. Alejandro Strachan Deputy Director, nanoHUB (2020 R&D 100 winner) -- Alejandro Strachan Professor of Materials Engineering, Purdue University Network for Computational Nanotechnology - nanohub.org<http://nanohub.org> Center for Predictive Materials and Devices (c-PRIMED)

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