Memristor-Based Multilayer Neural Networks With Online Gradient Descent Training

By Daniel Soudry, Dotan Di Castro, Asaf Gal, Avinoam Kolodny, Shahar Kvatinsky. 2015

244 citations recorded by the source corpus.

Subfields: Cellular and Molecular Neuroscience, Electrical and Electronic Engineering

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cellular and Molecular Neuroscience

1. Saransh Gupta
2. Pravin S. Mane
3. Nishil Talati
4. Yoshua Bengio
5. Shahar Kvatinsky

In Electrical and Electronic Engineering

1. Saransh Gupta
2. Pravin S. Mane
3. Nishil Talati
4. Ran El-Yaniv
5. Yoshua Bengio
6. Daniel Soudry
7. Matthieu Courbariaux
8. Itay Hubara

Citations in the corpus

• Binarized Neural Networks (2016, background): “Currently, saving the full precision requires relatively high energy resources (although, novel memory devices might be used to alleviate this issue in the future; see Soudry et al. (2015)).”
• A Survey of Neuromorphic Computing and Neural Networks in Hardware (2017, unknown): “[952], [959], [983], [1050], [1055], [1295], [1303], [1336],”
• Logic Design Within Memristive Memories Using Memristor-Aided loGIC (MAGIC) (2016, methodology): “Additionally, memristors with high adaptive thresholds can be used to mimic the higher order behavior of synapses and thus can be utilized efficiently in neuromorphic systems [9]–[11].”