Efficient architectures and implementation of arithmetic functions approximation based stochastic computing

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dc.contributor.author Luong, Tieu-Khanh
dc.contributor.author Nguyen, Van-Tinh
dc.contributor.author Nguyen, Anh-Thai
dc.contributor.author Popovici, Emanuel M.
dc.date.accessioned 2019-10-21T14:13:58Z
dc.date.available 2019-10-21T14:13:58Z
dc.date.issued 2019-07
dc.identifier.citation Luong, T., Nguyen, V., Nguyen, A. and Popovici, E. (2019) 'Efficient Architectures and Implementation of Arithmetic Functions Approximation Based Stochastic Computing'. 2019 IEEE 30th International Conference on Application-specific Systems, Architectures and Processors (ASAP), New York, USA, 15-17 July, pp. 281-287. doi: 10.1109/ASAP.2019.00018 en
dc.identifier.startpage 281 en
dc.identifier.endpage 287 en
dc.identifier.issn 2160-052X
dc.identifier.uri http://hdl.handle.net/10468/8805
dc.identifier.doi 10.1109/ASAP.2019.00018 en
dc.description.abstract Stochastic computing (SC) has emerged as a potential alternative to binary computing for a number of low-power embedded systems, DSP, neural networks and communications applications. In this paper, a new method, associated architectures and implementations of complex arithmetic functions, such as exponential, sigmoid and hyperbolic tangent functions are presented. Our approach is based on a combination of piecewise linear (PWL) approximation as well as a polynomial interpolation based (Lagrange interpolation) methods. The proposed method aims at reducing the number of binary to stochastic converters. This is the most power sensitive module in an SC system. The hardware implementation for each complex arithmetic function is then derived using the 65nm CMOS technology node. In terms of accuracy, the proposed approach outperforms other well-known methods by 2 times on average. The power consumption of the implementations based on our method is decreased on average by 40 % comparing to other previous solutions. Additionally, the hardware complexity of our proposed method is also improved (40 % on average) while the critical path of the proposed method is slightly increased by 2.5% on average when comparing to other methods. en
dc.format.mimetype application/pdf en
dc.language.iso en en
dc.publisher Institute of Electrical and Electronics Engineers (IEEE) en
dc.relation.uri https://ieeexplore.ieee.org/abstract/document/8825149
dc.rights © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. en
dc.subject Computer architecture en
dc.subject Digital arithmetic en
dc.subject Embedded systems en
dc.subject Function approximation en
dc.subject Interpolation en
dc.subject Polynomials en
dc.subject Stochastic processes en
dc.subject Communications applications en
dc.subject Associated architectures en
dc.subject Complex arithmetic function en
dc.subject Exponential functions en
dc.subject Sigmoid functions en
dc.subject Hyperbolic tangent functions en
dc.subject Piecewise linear approximation en
dc.subject Polynomial interpolation en
dc.subject Stochastic converters en
dc.subject Power sensitive module en
dc.subject SC system en
dc.subject Hardware implementation en
dc.subject Power consumption en
dc.subject Hardware complexity en
dc.subject Arithmetic functions approximation en
dc.subject Stochastic computing en
dc.subject Binary computing en
dc.subject Low-power embedded systems en
dc.subject Neural networks en
dc.subject CMOS technology node en
dc.subject Hardware en
dc.subject Logic gates en
dc.subject Complexity theory en
dc.subject Power demand en
dc.subject Stochastic computing en
dc.subject VLSI en
dc.subject Arithmetic en
dc.subject Low power en
dc.subject Efficient architectures en
dc.subject Sigmoid function en
dc.subject Piecewise linear approximation en
dc.subject Lagrange interpolations en
dc.title Efficient architectures and implementation of arithmetic functions approximation based stochastic computing en
dc.type Conference item en
dc.internal.authorcontactother Tieu-Khanh Luong, Electrical & Electronic Engineering, University College Cork, Cork, Ireland. +353-21-490-3000 Email: e.popovici@ucc.ie en
dc.internal.availability Full text available en
dc.date.updated 2019-10-21T14:07:55Z
dc.description.version Accepted Version en
dc.internal.rssid 499908272
dc.description.status Peer reviewed en
dc.internal.copyrightchecked No
dc.internal.licenseacceptance Yes en
dc.internal.conferencelocation New York, USA en
dc.internal.IRISemailaddress e.popovici@ucc.ie en


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