400 citations · 1.4k across the 22 of their papers we have counts for
22 papers
Integration of Physics-Derived Memristor Models with Machine Learning Frameworks
Zhenming Yu, Stephan Menzel, John Paul Strachan +1
Simulation frameworks such MemTorch, DNN+NeuroSim, and aihwkit are commonly used to facilitate the end-to-end co-design of memristive machine learning (ML) accelerators. These simu…
Heterogeneously Integrated Memristive Laser on Silicon with Non-Volatile Wavelength Tuning
Bassem Tossoun, Di Liang, Xia Sheng +2
The von-Neumann bottleneck has constrained computing systems from efficiently operating on the increasingly large demand in data from networks and devices. Silicon (Si) photonics o…
Analog Feedback-Controlled Memristor programming Circuit for analog Content Addressable Memory
Jiaao Yu, Paul-Philipp Manea, Sara Ameli +3
Recent breakthroughs in associative memories suggest that silicon memories are coming closer to human memories, especially for memristive Content Addressable Memories (CAMs) which…
High-Speed and Energy-Efficient Non-Volatile Silicon Photonic Memory Based on Heterogeneously Integrated Memresonator
Bassem Tossoun, Di Liang, Stanley Cheung +4
Recently, interest in programmable photonics integrated circuits has grown as a potential hardware framework for deep neural networks, quantum computing, and field programmable arr…
Experimentally realized memristive memory augmented neural network
Ruibin Mao, Bo Wen, Yahui Zhao +8
Lifelong on-device learning is a key challenge for machine intelligence, and this requires learning from few, often single, samples. Memory augmented neural network has been propos…
Prospects for Analog Circuits in Deep Networks
Shih-Chii Liu, John Paul Strachan, Arindam Basu
Operations typically used in machine learning al-gorithms (e.g. adds and soft max) can be implemented bycompact analog circuits. Analog Application-Specific Integrated Circuit (ASI…
2022 Roadmap on Neuromorphic Computing and Engineering
Dennis V. Christensen, Regina Dittmann, Bernabé Linares-Barranco +56
Modern computation based on the von Neumann architecture is today a mature cutting-edge science. In the Von Neumann architecture, processing and memory units are implemented as sep…
Tree-based machine learning performed in-memory with memristive analog CAM
Giacomo Pedretti, Catherine E. Graves, Can Li +5
Tree-based machine learning techniques, such as Decision Trees and Random Forests, are top performers in several domains as they do well with limited training datasets and offer im…
PANTHER: A Programmable Architecture for Neural Network Training Harnessing Energy-efficient ReRAM
Aayush Ankit, Izzat El Hajj, Sai Rahul Chalamalasetti +7
The wide adoption of deep neural networks has been accompanied by ever-increasing energy and performance demands due to the expensive nature of training them. Numerous special-purp…
Thermodynamic Computing
Tom Conte, Erik DeBenedictis, Natesh Ganesh +36
The hardware and software foundations laid in the first half of the 20th Century enabled the computing technologies that have transformed the world, but these foundations are now u…
Analog content addressable memories with memristors
Can Li, Catherine E. Graves, Xia Sheng +4
A content-addressable-memory compares an input search word against all rows of stored words in an array in a highly parallel manner. While supplying a very powerful functionality f…
Harnessing Intrinsic Noise in Memristor Hopfield Neural Networks for Combinatorial Optimization
Fuxi Cai, Suhas Kumar, Thomas Van Vaerenbergh +8
We describe a hybrid analog-digital computing approach to solve important combinatorial optimization problems that leverages memristors (two-terminal nonvolatile memories). While p…
PUMA: A Programmable Ultra-efficient Memristor-based Accelerator for Machine Learning Inference
Aayush Ankit, Izzat El Hajj, Sai Rahul Chalamalasetti +8
Memristor crossbars are circuits capable of performing analog matrix-vector multiplications, overcoming the fundamental energy efficiency limitations of digital logic. They have be…
Long short-term memory networks in memristor crossbars
Can Li, Zhongrui Wang, Mingyi Rao +14
Recent breakthroughs in recurrent deep neural networks with long short-term memory (LSTM) units has led to major advances in artificial intelligence. State-of-the-art LSTM models w…
Oxygen migration during resistance switching and failure of hafnium oxide memristors
Suhas Kumar, Ziwen Wang, Xiaopeng Huang +7
While the recent establishment of the role of thermophoresis/diffusion-driven oxygen migration during resistance switching in metal oxide memristors provided critical insights requ…
Spatially uniform resistance switching of low current, high endurance titanium-niobium-oxide memristors
Suhas Kumar, Noraica Davila, Ziwen Wang +6
We analyzed micrometer-scale titanium-niobium-oxide prototype memristors, which exhibited low write-power (<3 μW) and energy (<200 fJ/bit/μm2), low read-power (~nW), and high endur…
Conduction Channel Formation and Dissolution Due to Oxygen Thermophoresis/Diffusion in Hafnium Oxide Memristors
Suhas Kumar, Ziwen Wang, Xiaopeng Huang +7
Transition metal oxide memristors, or resistive random-access memory (RRAM) switches, are under intense development for storage-class memory because of their favorable operating po…
Direct Observation of Localized Radial Oxygen Migration in Functioning Tantalum Oxide Memristors
Suhas Kumar, Catherine E. Graves, John Paul Strachan +6
Oxygen migration in tantalum oxide, a promising next-generation storage material, is studied using in-operando x-ray absorption spectromicroscopy and is used to microphysically des…
The phase transition in VO2 probed using x-ray, visible and infrared radiations
Suhas Kumar, John Paul Strachan, A. L. David Kilcoyne +5
Vanadium dioxide (VO2) is a model system that has been used to understand closely-occurring multiband electronic (Mott) and structural (Peierls) transitions for over half a century…
Sequential Electronic and Structural Transitions in VO2 Observed Using X-ray Absorption Spectromicroscopy
Suhas Kumar, John Paul Strachan, Matthew D. Pickett +3
The popular dual electronic (Mott) and structural (Peierls) transitions in VO2 are explored using x-ray absorption spectromicroscopy with high spatial and spectral resolutions. It…
Local Temperature Redistribution and Structural Transition During Joule-Heating-Driven Conductance Switching in VO2
Suhas Kumar, Matthew D. Pickett, John Paul Strachan +3
Joule-heating induced conductance-switching is studied in VO2, a Mott insulator. Complementary in-situ techniques including optical characterization, blackbody microscopy, scanning…
In-operando synchronous time-multiplexed O K-edge x-ray absorption spectromicroscopy of functioning tantalum oxide memristors
Suhas Kumar, Catherine E. Graves, John Paul Strachan +4
Memristors are receiving keen interest because of their potential varied applications and promising large-scale information storage capabilities. Tantalum oxide is a memristive mat…