Architecture and simulation of a hybrid memristive multiplier network using redundant number representation
Fey D (2015)
Publication Type: Conference contribution
Publication year: 2015
Publisher: Institute of Electrical and Electronics Engineers Inc.
Edited Volumes: Proceedings of the International Joint Conference on Neural Networks
Book Volume: 2015-September
Pages Range: 1-6
Conference Proceedings Title: Neural Networks (IJCNN),
Event location: Killarney, Ireland
ISBN: 978-1-4799-1959-8
DOI: 10.1109/IJCNN.2015.7280789
Abstract
The paper proposes an architecture for a multiplier-adder network that can be used for the design of a digital neuron cell. The core of the multiplier is based on a hybrid memristor network, in which digital CMOS logic is combined with multi-stable storing memristor devices. The multi-bit storing feature of memristors is favoured since it simplifies the realisation of ternary data. Using such a ternary number system in binary logic leads to a redundant number representation (RNR) that allows to speed up multiplications since they are reduced to adders working in constant time independent of the word length. For the verification of the multiplier architecture an own special simulation system was developed in C++ allowing flexible design and fast analogue simulation of large complex memristor networks. The superiority of the hybrid memristive architecture in terms of latency and bandwidth compared to an adder with carry-look-ahead technique is analytically shown.
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How to cite
APA:
Fey, D. (2015). Architecture and simulation of a hybrid memristive multiplier network using redundant number representation. In Neural Networks (IJCNN), (pp. 1-6). Killarney, Ireland, IE: Institute of Electrical and Electronics Engineers Inc..
MLA:
Fey, Dietmar. "Architecture and simulation of a hybrid memristive multiplier network using redundant number representation." Proceedings of the 2015 International Joint Conference on Neural Networks, Killarney, Ireland Institute of Electrical and Electronics Engineers Inc., 2015. 1-6.
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