Name
Title | ||
|---|---|---|
Stochastic Learning in Neuromorphic Hardware via Spike Timing Dependent Plasticity With RRAM Synapses. |
Abstract | ||
|---|---|---|
Hardware processors for neuromorphic computing are gaining significant interest as they offer the possibility of real in-memory computing, thus by-passing the limitations of speed and energy consumption of the von Neumann architecture. One of the major limitations of current neuromorphic technology is the lack of bio-realistic and scalable devices to improve the current design of artificial synaps... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/JETCAS.2017.2773124 | IEEE Journal on Emerging and Selected Topics in Circuits and Systems |
Keywords | Field | DocType |
Neuromorphics,Neurons,Hardware,Resistance,Switches,Phase change materials | Computer science,Neuromorphic engineering,Electronic engineering,Proof of concept,Learning rule,Unsupervised learning,Spike-timing-dependent plasticity,Von Neumann architecture,Scalability,Resistive random-access memory | Journal |
Volume | Issue | ISSN |
8 | 1 | 2156-3357 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
9 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Giacomo Pedretti | 1 | 1 | 1.36 |
| Valerio Milo | 2 | 2 | 1.06 |
| Stefano Ambrogio | 3 | 33 | 5.83 |
| Roberto Carboni | 4 | 1 | 0.69 |
| Stefano Bianchi | 5 | 0 | 0.34 |
| Alessandro Calderoni | 6 | 0 | 0.34 |
| Nirmal Ramaswamy | 7 | 1 | 1.36 |
| alessandro s spinelli | 8 | 24 | 5.13 |
| Daniele Ielmini | 9 | 15 | 9.64 |