Excitatory and Inhibitory Memristive Synapses for Spiking Neural Networks
被引:0
作者:
Lecerf, Gwendal
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机构:
Univ Bordeaux, IMS, UMR 5218, F-33400 Talence, FranceUniv Bordeaux, IMS, UMR 5218, F-33400 Talence, France
Lecerf, Gwendal
[1
]
Tomas, Jean
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h-index: 0
机构:
Univ Bordeaux, IMS, UMR 5218, F-33400 Talence, FranceUniv Bordeaux, IMS, UMR 5218, F-33400 Talence, France
Tomas, Jean
[1
]
Saighi, Sylvain
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h-index: 0
机构:
Univ Bordeaux, IMS, UMR 5218, F-33400 Talence, FranceUniv Bordeaux, IMS, UMR 5218, F-33400 Talence, France
Saighi, Sylvain
[1
]
机构:
[1] Univ Bordeaux, IMS, UMR 5218, F-33400 Talence, France
来源:
2013 IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS (ISCAS)
|
2013年
关键词:
D O I:
暂无
中图分类号:
TM [电工技术];
TN [电子技术、通信技术];
学科分类号:
0808 ;
0809 ;
摘要:
Neuromorphic chips are composed of silicon neurons, synapses and memories for synaptic weight. Moreover we can find a fourth part dedicated to synaptic plasticity algorithm. Even though we can find some low-power silicon neurons, the power consumption reduction of synapses, memories and plasticity algorithm is not enough explored. Since memristor coming-out in 2008, neuromorphic designers investigate the possibility of using memristors as plastic synapses due to their intrinsic property of plasticity. This nanocomponent gathers the function of synapse, the weight storage and the plasticity. So far, the proposed solutions cannot manage both excitatory and inhibitory memristive synapses with one single design. In this paper we will present an elegant solution based on current conveyor (CCII) for driving memristor as excitatory or inhibitory synapses following the neural network implementation.