tag: deep learning

DNN-Opt, A Novel Deep Neural Network (DNN) Based Black-Box Optimization Framework For Analog Sizing
By Technical Paper Link - 19 Aug, 2022 - Comments: 0

This technical paper titled "DNN-Opt: An RL Inspired Optimization for Analog Circuit Sizing using Deep Neural Networks" is co-authored from researchers at The University of Texas at Austin, Intel, University of Glasgow. The paper was a best paper candidate at DAC 2021. "In this paper, we present DNN-Opt, a novel Deep Neural Network (DNN) based black-box optimization framework for analog sizi... » read more

New Uses For AI In Chips
By John Koon and Ed Sperling - 11 Aug, 2022 - Comments: 1

Artificial intelligence is being deployed across a number of new applications, from improving performance and reducing power in a wide range of end devices to spotting irregularities in data movement for security reasons. While most people are familiar with using machine learning and deep learning to distinguish between cats and dogs, emerging applications show how this capability can be use... » read more

Low Power HW Accelerator for FP16 Matrix Multiplications For Tight Integration Within RISC-V Cores
By Technical Paper Link - 03 Aug, 2022 - Comments: 0

This new technical paper titled "RedMulE: A Compact FP16 Matrix-Multiplication Accelerator for Adaptive Deep Learning on RISC-V-Based Ultra-Low-Power SoCs" was published by researchers at University of Bologna and ETH Zurich. According to their abstract: "One of the key stumbling stones is the need for parallel floating-point operations, which are considered unaffordable on sub-100 mW extre... » read more

Analog Deep Learning Processor (MIT)
By Technical Paper Link - 29 Jul, 2022 - Comments: 0

A team of researchers at MIT are working on hardware for artificial intelligence that offers faster computing with less power. The analog deep learning technique involves sending protons through solids at extremely fast speeds.  “The working mechanism of the device is electrochemical insertion of the smallest ion, the proton, into an insulating oxide to modulate its electronic conductivity... » read more

Distilling The Essence Of Four DAC Keynotes
By Brian Bailey - 28 Jul, 2022 - Comments: 0

Chip design and verification are facing a growing number of challenges. How they will be solved — particularly with the addition of machine learning — is a major question for the EDA industry, and it was a common theme among four keynote speakers at this month's Design Automation Conference. DAC has returned as a live event, and this year's keynotes involved the leaders of a systems comp... » read more

Identifying PCB Defects with a Deep Learning Single-Step Detection Model
By Technical Paper Link - 22 Jul, 2022 - Comments: 0

This new technical paper titled "End-to-end deep learning framework for printed circuit board manufacturing defect classification" is from researchers at École de technologie supérieure (ÉTS) in Montreal, Quebec. Abstract "We report a complete deep-learning framework using a single-step object detection model in order to quickly and accurately detect and classify the types of manufacturi... » read more

Neuromorphic Computing: Challenges, Opportunities Including Materials, Algorithms, Devices & Ethics
By Technical Paper Link - 06 Jul, 2022 - Comments: 0

This new research paper titled "2022 roadmap on neuromorphic computing and engineering" is from numerous researchers at Technical University of Denmark, Instituto de Microelectrónica de Sevilla, CSIC, University of Seville, and many others. Partial Abstract: "The aim of this roadmap is to present a snapshot of the present state of neuromorphic technology and provide an opinion on the chall... » read more

Finding Wafer Defects Using Quantum DL
By Technical Paper Link - 13 Jun, 2022 - Comments: 0

New research paper titled "Semiconductor Defect Detection by Hybrid Classical-Quantum Deep Learning" by researchers at National Tsing Hua University. Abstract "With the rapid development of artificial intelligence and autonomous driving technology, the demand for semiconductors is projected to rise substantially. However, the massive expansion of semiconductor manufacturing and the develo... » read more

Deep Learning Applications For Material Sciences: Methods, Recent Developments
By Technical Paper Link - 10 Jun, 2022 - Comments: 0

New technical paper titled "Recent advances and applications of deep learning methods in materials science" from researchers at NIST, UCSD, Lawrence Berkeley National Laboratory, Carnegie Mellon University, Northwestern University, and Columbia University. Abstract "Deep learning (DL) is one of the fastest-growing topics in materials data science, with rapidly emerging applications spanning... » read more

Hybrid Method For More Reliable Virtual Sensors Within Vehicle Dynamics Control Systems
By Technical Paper Link - 25 May, 2022 - Comments: 0

New technical paper titled "Ensuring the Reliability of Virtual Sensors Based on Artificial Intelligence within Vehicle Dynamics Control Systems" from University of Duisburg-Essen. Abstract "The use of virtual sensors in vehicles represents a cost-effective alternative to the installation of physical hardware. In addition to physical models resulting from theoretical modeling, artificial in... » read more

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