publication . Preprint . 2015

Energy-efficient neuromorphic classifiers

Martí, Daniel; Rigotti, Mattia; Seok, Mingoo; Fusi, Stefano;
Open Access English
  • Published: 01 Jul 2015
Abstract
Neuromorphic engineering combines the architectural and computational principles of systems neuroscience with semiconductor electronics, with the aim of building efficient and compact devices that mimic the synaptic and neural machinery of the brain. Neuromorphic engineering promises extremely low energy consumptions, comparable to those of the nervous system. However, until now the neuromorphic approach has been restricted to relatively simple circuits and specialized functions, rendering elusive a direct comparison of their energy consumption to that used by conventional von Neumann digital machines solving real-world tasks. Here we show that a recent technolo...
Subjects
free text keywords: Quantitative Biology - Neurons and Cognition, Computer Science - Neural and Evolutionary Computing
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51 references, page 1 of 4

1. Mead C (1989) Analog VLSI implementation of neural systems (Addison Wesley Publishing Company).

2. Indiveri G, et al. (2011) Neuromorphic silicon neuron circuits. Front. Neurosci. 5. [OpenAIRE]

3. Livi P, Indiveri G (2009) A current-mode conductance-based silicon neuron for address-event neuromorphic systems pp 2898{2901. [OpenAIRE]

4. Rangan V, Ghosh A, Aparin V, Cauwenberghs G (2010) A subthreshold aVLSI implementation of the Izhikevich simple neuron model pp 4164{4167.

5. Chicca E, Stefanini F, Indiveri G (2014) Neuromorphic electronic circuits for building autonomous cognitive systems. Proceedings of the IEEE PP:1{22.

6. Merolla PA, et al. (2014) A million spiking-neuron integrated circuit with a scalable communication network and interface. Science 345:668{673. [OpenAIRE]

7. Jaeger H, Haas H (2004) Harnessing nonlinearity: predicting chaotic systems and saving energy in wireless communication. Science 304:78{80.

8. Buonomano DV, Maass W (2009) State-dependent computations: spatiotemporal processing in cortical networks. Nat Rev Neurosci 10:113{125. [OpenAIRE]

9. Barak O, Rigotti M, Fusi S (2013) The sparseness of mixed selectivity neurons controls the generalization{discrimination trade-o . J. Neurosci. 33:3844{3856.

10. Arthur JV, et al. (2012) Building block of a programmable neuromorphic substrate: A digital neurosynaptic core (IEEE), pp 1{8.

11. Eliasmith C, et al. (2012) A large-scale model of the functioning brain. Science 338:1202{1205. [OpenAIRE]

12. Fusi S, Mattia M (1999) Collective behavior of networks with linear (VLSI) integrate-and- re neurons. Neural Comput. 11:633{652. [OpenAIRE]

13. Sompolinsky H (1986) Neural networks with non-linear synapses and static noise. Phys. Rev. A 34:2571. [OpenAIRE]

14. Amit DJ, Fusi S (1994) Learning in neural networks with material synapses. Neural Computation 6:957{982. [OpenAIRE]

15. Fusi S (2002) Hebbian spike-driven synaptic plasticity for learning patterns of mean ring rates. Biological cybernetics 87:459{470.

51 references, page 1 of 4
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