Proceedings Article10.1109/ISCAS.2015.7168559
The applications of memristor devices in next-generation cortical processor designs
Hai Li,Beiye Liu,Xiaoxiao Liu,Mengjie Mao,Yi Chen,Qing Wu,Qinru Qiu +6 more
- 24 May 2015
- pp 17-20
8
TL;DR: This paper presents the implementation of cortical processor augmented with neuromorphic computing accelerators (NCAs) for cognitive applications, including the design details and basic operations of the NCA based on memristor crossbars and the integration between conventional pipeline and NCAs.
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Abstract: Discovery of memristor opened a new era of the research on universal memory thanks to many attractive properties demonstrated by this emerging device In this paper, we switch our research focus to neuromorphic computing, which, same as memory technology, significantly benefits from the technical advances of memristor Particularly, we present the implementation of cortical processor augmented with neuromorphic computing accelerators (NCAs) for cognitive applications, including: 1) the design details and basic operations of the NCA based on memristor crossbars; and 2) the integration between conventional pipeline and NCAs At the end, we also discuss the scalability of our proposed NCA designs
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A Survey of Neuromorphic Computing and Neural Networks in Hardware.
Catherine D. Schuman,Thomas E. Potok,Robert M. Patton,J. Douglas Birdwell,Mark Edward Dean,Garrett S. Rose,James S. Plank +6 more
TL;DR: An exhaustive review of the research conducted in neuromorphic computing since the inception of the term is provided to motivate further work by illuminating gaps in the field where new research is needed.
686
Harmonica: A Framework of Heterogeneous Computing Systems With Memristor-Based Neuromorphic Computing Accelerators
Xiaoxiao Liu,Mengjie Mao,Beiye Liu,Boxun Li,Yu Wang,Hao Jiang,Mark Barnell,Qing Wu,Jianhua Yang,Hai Li,Yi Chen +10 more
TL;DR: Harmonica-a framework of heterogeneous computing system enhanced by memristor-based neuromorphic computing accelerators (NCAs) is presented, which are superior to the designs with either digital neural processing units (D-NPUs) or MBC arrays cooperating with a digital interconnection network.
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Stochastic implementation of the activation function for artificial neural networks
Injune Yeo,Sang-Gyun Gi,Byung-Geun Lee,Myonglae Chu +3 more
- 01 Oct 2016
TL;DR: Circuit techniques for realizing four different types of AFs, such as the step, identity, rectified-linear unit (ReLU), and the sigmoid, based on stochastic computing are presented.
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Advent of memristor based synapses on neuromorphic engineering
S. Vidya,Mohammed Riyaz Ahmed +1 more
- 01 Aug 2017
TL;DR: This work reviews the recent progress on the development of 2-terminal resistive device called memristor to mimic neuronal synapse based on Spike-timing-dependent plasticity protocol as an asymmetric form of hebbian learning.
3
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A stochastic implementation method of an activation function for an artificial neural network and a system including the same
Yeo In June,Lee Byung Geun,Gi Sang Gyun +2 more
- 05 Nov 2018
TL;DR: In this paper, an activation function for an artificial neural network can be implemented without an analog-to-digital converter by adjusting comparator inputs constituting the neural network, which can implement four different types of active functions.
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References
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TL;DR: It is shown, using a simple analytical example, that memristance arises naturally in nanoscale systems in which solid-state electronic and ionic transport are coupled under an external bias voltage.
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TL;DR: In this article, the memristor is introduced as the fourth basic circuit element and an electromagnetic field interpretation of this relationship in terms of a quasi-static expansion of Maxwell's equations is presented.
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Memristor Crossbar-Based Neuromorphic Computing System: A Case Study
TL;DR: The results show that the hardware-based training scheme proposed in the paper can alleviate and even cancel out the majority of the noise issue and apply it to brain-state-in-a-box (BSB) neural networks.
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