81 citations · 153 across the 7 of their papers we have counts for
10 papers
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…
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…