tag: reinforcement learning

Sibyl, a lightweight, reinforcement learning-based data placement technique for hybrid storage systems (ETH Zurich)
By Technical Paper Link - 06 Jul, 2022 - Comments: 0

New research paper titled "Sibyl: Adaptive and Extensible Data Placement in Hybrid Storage Systems Using Online Reinforcement Learning" from researchers at ETH Zurich, Eindhoven University of Technology, and LIRMM, Univ. Montpellier, CNRS. Abstract "Hybrid storage systems (HSS) use multiple different storage devices to provide high and scalable storage capacity at high performance. Recent r... » read more

Deep Reinforcement Learning to Dynamically Configure NoC Resources
By Technical Paper Link - 17 Jun, 2022 - Comments: 0

New research paper titled "Deep Reinforcement Learning Enabled Self-Configurable Networks-on-Chip for High-Performance and Energy-Efficient Computing Systems" from Md Farhadur Reza at Eastern Illinois University. Find the open access technical paper here. Published June 2022. M. F. Reza, "Deep Reinforcement Learning Enabled Self-Configurable Networks-on-Chip for High-Performance and Energ... » read more

AlphaGo Game Influences Argonne’s New AI Tool For Materials Discovery
By Technical Paper Link - 10 Jun, 2022 - Comments: 0

Research paper titled "Learning in continuous action space for developing high dimensional potential energy models" from researchers at Argonne National Lab with contributions from Oak Ridge National Laboratory. Abstract "Reinforcement learning (RL) approaches that combine a tree search with deep learning have found remarkable success in searching exorbitantly large, albeit discrete action ... » read more

Improving PPA In Complex Designs With AI
By John Koon - 14 Feb, 2022 - Comments: 0

The goal of chip design always has been to optimize power, performance, and area (PPA), but results can vary greatly even with the best tools and highly experienced engineering teams. Optimizing PPA involves a growing number of tradeoffs that can vary by application, by availability of IP and other components, as well as the familiarity of engineers with different tools and methodologies. Fo... » read more

Raising The Bar With The Next Generation Of AI For Chip Design
By Synopsys - 22 Nov, 2021 - Comments: 0

The semiconductor industry is enjoying renewed growth despite chip shortages plaguing everything from cars to kitchen appliances. But while the chips themselves continue to get faster and smarter, the chip design process itself hasn’t changed that much in 20+ years. It typically takes 2-3 years to design a chip with a large engineering team and tens or hundreds of millions of dollars to get a... » read more

Easier And Faster Ways To Train AI
By Bryon Moyer - 04 Nov, 2021 - Comments: 0

Training an AI model takes an extraordinary amount of effort and data. Leveraging existing training can save time and money, accelerating the release of new products that use the model. But there are a few ways this can be done, most notably through transfer and incremental learning, and each of them has its applications and tradeoffs. Transfer learning and incremental learning both take pre... » read more

Power/Performance Bits: Aug. 10
By Jesse Allen - 10 Aug, 2020 - Comments: 0

Flexible electrodes for thin films Researchers from the University of Queensland and ARC Centre of Excellence in Exciton Science (University of Melbourne) developed a material for flexible, recyclable, transparent electrodes that could be used in things like solar panels, touchscreens, and smart windows. Eser Akinoglu of the ARC Centre of Excellence in Exciton Science said, "The performance... » read more

Are Better Machine Training Approaches Ahead?
By Bryon Moyer - 01 Jul, 2020 - Comments: 1

We live in a time of unparalleled use of machine learning (ML), but it relies on one approach to training the models that are implemented in artificial neural networks (ANNs) — so named because they’re not neuromorphic. But other training approaches, some of which are more biomimetic than others, are being developed. The big question remains whether any of them will become commercially viab... » read more

Finding And Fixing ML’s Flaws
By Ed Sperling - 02 May, 2018 - Comments: 0

OneSpin CEO Raik Brinkmann sat down with Semiconductor Engineering to discuss how to make machine learning more robust, predictable and consistent, and new ways to identify and fix problems that may crop up as these systems are deployed. What follows are excerpts of that conversation. SE: How do we make sure devices developed with machine learning behave as they're supposed to, and how do we... » read more

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