400 citations · 1.4k across the 22 of their papers we have counts for
9 papers · 1 filter
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…
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…
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…
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…
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…