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Physical Computing: Unifying Real Number Computation to Enable Energy Efficient Computing. JOURNAL OF LOW POWER ELECTRONICS AND APPLICATIONS 2021. [DOI: 10.3390/jlpea11020014] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Physical computing unifies real value computing including analog, neuromorphic, optical, and quantum computing. Many real-valued techniques show improvements in energy efficiency, enable smaller area per computation, and potentially improve algorithm scaling. These physical computing techniques suffer from not having a strong computational theory to guide application development in contrast to digital computation’s deep theoretical grounding in application development. We consider the possibility of a real-valued Turing machine model, the potential computational and algorithmic opportunities of these techniques, the implications for implementation applications, and the computational complexity space arising from this model. These techniques have shown promise in increasing energy efficiency, enabling smaller area per computation, and potentially improving algorithm scaling.
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Amiri M, Davande H, Sadeghian A, Chartier S. Feedback associative memory based on a new hybrid model of generalized regression and self-feedback neural networks. Neural Netw 2010; 23:892-904. [DOI: 10.1016/j.neunet.2010.05.005] [Citation(s) in RCA: 26] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2010] [Revised: 05/02/2010] [Accepted: 05/05/2010] [Indexed: 11/29/2022]
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Amiri M, Saeb S, Yazdanpanah MJ, Seyyedsalehi SA. Analysis of the dynamical behavior of a feedback auto-associative memory. Neurocomputing 2008. [DOI: 10.1016/j.neucom.2007.07.027] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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Amiri M, Davande H, Sadeghian A, Seyyedsalehi SA. Auto-Associative Neural Network Based on New Hybrid Model of SFNN and GRNN. ACTA ACUST UNITED AC 2007. [DOI: 10.1109/ijcnn.2007.4371379] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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Sanqing Hu, Jun Wang. Global stability of a class of discrete-time recurrent neural networks. ACTA ACUST UNITED AC 2002. [DOI: 10.1109/tcsi.2002.801284] [Citation(s) in RCA: 42] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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Liang Jin, Gupta M. Stable dynamic backpropagation learning in recurrent neural networks. ACTA ACUST UNITED AC 1999; 10:1321-34. [DOI: 10.1109/72.809078] [Citation(s) in RCA: 84] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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Liang Jin, Gupta M. Equilibrium capacity of analog feedback neural networks. ACTA ACUST UNITED AC 1996; 7:782-7. [DOI: 10.1109/72.501736] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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Chen ZY, Kwong CP, Xu ZB. Multiple-valued feedback and recurrent correlation neural networks. Neural Comput Appl 1995. [DOI: 10.1007/bf01414649] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Sperduti A. Stability properties of labeling recursive auto-associative memory. ACTA ACUST UNITED AC 1995; 6:1452-60. [DOI: 10.1109/72.471363] [Citation(s) in RCA: 14] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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Liang Jin, Nikiforuk P, Gupta M. Absolute stability conditions for discrete-time recurrent neural networks. ACTA ACUST UNITED AC 1994; 5:954-64. [DOI: 10.1109/72.329693] [Citation(s) in RCA: 63] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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