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Erschienen in: Cognitive Computation 5/2017

27.05.2017

Storages Are Not Forever

verfasst von: Erik Cambria, Anupam Chattopadhyay, Eike Linn, Bappaditya Mandal, Bebo White

Erschienen in: Cognitive Computation | Ausgabe 5/2017

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Abstract

Not unlike the concern over diminishing fossil fuel, information technology is bringing its own share of future worries. We chose to look closely into one concern in this paper, namely the limited amount of data storage. By a simple extrapolatory analysis, it is shown that we are on the way to exhaust our storage capacity in less than two centuries with current technology and no recycling. This can be taken as a note of caution to expand research initiative in several directions: firstly, bringing forth innovative data analysis techniques to represent, learn, and aggregate useful knowledge while filtering out noise from data; secondly, tap onto the interplay between storage and computing to minimize storage allocation; thirdly, explore ingenious solutions to expand storage capacity. Throughout this paper, we delve deeper into the state-of-the-art research and also put forth novel propositions in all of the abovementioned directions, including space- and time-efficient data representation, intelligent data aggregation, in-memory computing, extra-terrestrial storage, and data curation. The main aim of this paper is to raise awareness on the storage limitation we are about to face if current technology is adopted and the storage utilization growth rate persists. In the manuscript, we propose some storage solutions and a better utilization of storage capacity through a global DIKW hierarchy.

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Literatur
7.
Zurück zum Zitat Loth S, Baumann S, Lutz CP, Eigler DM, Heinrich AJ. Bistability in atomic-scale antiferromagnets. Science. 2012;335(6065):196–9.CrossRefPubMed Loth S, Baumann S, Lutz CP, Eigler DM, Heinrich AJ. Bistability in atomic-scale antiferromagnets. Science. 2012;335(6065):196–9.CrossRefPubMed
9.
Zurück zum Zitat Zhai Y, Ong Y-S, Tsang I. The emerging “big dimensionality”. IEEE Comput Intell Mag. 2014;9(3):14–26.CrossRef Zhai Y, Ong Y-S, Tsang I. The emerging “big dimensionality”. IEEE Comput Intell Mag. 2014;9(3):14–26.CrossRef
10.
Zurück zum Zitat Haralickand R, Shanmugam K, Dinstein I. Textural features for image classification. IEEE Trans Syst Man Cybern. 2007;3(6):610–21. Haralickand R, Shanmugam K, Dinstein I. Textural features for image classification. IEEE Trans Syst Man Cybern. 2007;3(6):610–21.
11.
Zurück zum Zitat Duda RO, Hart PE, Stork DG. Pattern classification. New York: Wiley; 2001. Duda RO, Hart PE, Stork DG. Pattern classification. New York: Wiley; 2001.
12.
Zurück zum Zitat Zhu M, Martinez AM. Optimal subclass discovery for discriminant analysis. In: Conference on computer vision and pattern recognition workshop, 2004. CVPRW ’04. 2004. p. 97–104. Zhu M, Martinez AM. Optimal subclass discovery for discriminant analysis. In: Conference on computer vision and pattern recognition workshop, 2004. CVPRW ’04. 2004. p. 97–104.
13.
Zurück zum Zitat Wang M, Li H-X, Chen X, Chen Y. Deep learning-based model reduction for distributed parameter systems. IEEE Trans Syst Man Cybern Syst. 2016;46(12):1664–74.CrossRef Wang M, Li H-X, Chen X, Chen Y. Deep learning-based model reduction for distributed parameter systems. IEEE Trans Syst Man Cybern Syst. 2016;46(12):1664–74.CrossRef
14.
Zurück zum Zitat Dai B, Li H, Wei L. Image processing unit for general-purpose representation and association system for recognizing low-resolution digits with visual information variability. IEEE Trans Syst Man Cybern Syst. 2016. Dai B, Li H, Wei L. Image processing unit for general-purpose representation and association system for recognizing low-resolution digits with visual information variability. IEEE Trans Syst Man Cybern Syst. 2016.
15.
Zurück zum Zitat Zhao H, Yuen PC. Incremental linear discriminant analysis for face recognition. IEEE Trans Syst Man Cybern Part B (Cybern). 2008;38(1):210–21.CrossRef Zhao H, Yuen PC. Incremental linear discriminant analysis for face recognition. IEEE Trans Syst Man Cybern Part B (Cybern). 2008;38(1):210–21.CrossRef
16.
Zurück zum Zitat Schölkopf B, Mika S, Burges C, Knirsch P, Müller K-R, Rätsch G, Smola A. Input space versus feature space in kernel-based methods. IEEE Trans Neural Netw. 1999;10:1000–17.CrossRefPubMed Schölkopf B, Mika S, Burges C, Knirsch P, Müller K-R, Rätsch G, Smola A. Input space versus feature space in kernel-based methods. IEEE Trans Neural Netw. 1999;10:1000–17.CrossRefPubMed
17.
Zurück zum Zitat Mika S, Ratsch G, Weston J, Scholkopf B, Mullers KR. Fisher discriminant analysis with kernels. In: Proceedings of the 1999 IEEE signal processing society workshop neural networks for signal processing IX. 1999. p. 41–48. Mika S, Ratsch G, Weston J, Scholkopf B, Mullers KR. Fisher discriminant analysis with kernels. In: Proceedings of the 1999 IEEE signal processing society workshop neural networks for signal processing IX. 1999. p. 41–48.
18.
Zurück zum Zitat Jiang XD, Mandal B, Kot A. Eigenfeature regularization and extraction in face recognition. IEEE Trans Pattern Anal Mach Intell. 2008;30(3):383–94.CrossRefPubMed Jiang XD, Mandal B, Kot A. Eigenfeature regularization and extraction in face recognition. IEEE Trans Pattern Anal Mach Intell. 2008;30(3):383–94.CrossRefPubMed
19.
Zurück zum Zitat Jiang XD, Mandal B, Kot A. Complete discriminant evaluation and feature extraction in kernel space for face recognition. Mach Vis Appl Springer. 2009;20(1):35–46.CrossRef Jiang XD, Mandal B, Kot A. Complete discriminant evaluation and feature extraction in kernel space for face recognition. Mach Vis Appl Springer. 2009;20(1):35–46.CrossRef
21.
Zurück zum Zitat Taigman Y, Yang M, Ranzato M, Wolf L. Deepface: closing the gap to human-level performance in face verification. In: CVPR. Columbus; 2014. p. 1701–1708. Taigman Y, Yang M, Ranzato M, Wolf L. Deepface: closing the gap to human-level performance in face verification. In: CVPR. Columbus; 2014. p. 1701–1708.
22.
Zurück zum Zitat Huang GB, Ramesh M, Berg Ta, Learned-Miller E. Labeled faces in the wild: a database for studying face recognition in unconstrained environments. Technical Report 07-49. University of Massachusetts, Amherst. 2007. Huang GB, Ramesh M, Berg Ta, Learned-Miller E. Labeled faces in the wild: a database for studying face recognition in unconstrained environments. Technical Report 07-49. University of Massachusetts, Amherst. 2007.
23.
Zurück zum Zitat Wolf L, Hassner T, Maoz I. Face recognition in unconstrained video with matched background similarity. In: IEEE Conference on computer vision and pattern recognition. 2011. p. 529–534. Wolf L, Hassner T, Maoz I. Face recognition in unconstrained video with matched background similarity. In: IEEE Conference on computer vision and pattern recognition. 2011. p. 529–534.
24.
Zurück zum Zitat Phillips PJ, Moon H, Rizvi S, Rauss P. The FERET evaluation methodology for face recognition algorithms. IEEE Trans Pattern Anal Mach Intell. 2000;22(10):1090–1104.CrossRef Phillips PJ, Moon H, Rizvi S, Rauss P. The FERET evaluation methodology for face recognition algorithms. IEEE Trans Pattern Anal Mach Intell. 2000;22(10):1090–1104.CrossRef
26.
27.
Zurück zum Zitat Swets DL, Weng J. Using discriminant eigenfeatures for image retrieval. IEEE Trans Pattern Anal Mach Intell. 1996;18(8):831–6.CrossRef Swets DL, Weng J. Using discriminant eigenfeatures for image retrieval. IEEE Trans Pattern Anal Mach Intell. 1996;18(8):831–6.CrossRef
28.
Zurück zum Zitat Mandal B, Zhikai W, Li L, Kassim A. Whole space subclass discriminant analysis for face recognition. In: IEEE International conference on image processing (ICIP). Quebec City. Mandal B, Zhikai W, Li L, Kassim A. Whole space subclass discriminant analysis for face recognition. In: IEEE International conference on image processing (ICIP). Quebec City.
29.
Zurück zum Zitat Balduzzi D. 2013. Randomized co-training: from cortical neurons to machine learning and back again. arXiv:1310.6536. Balduzzi D. 2013. Randomized co-training: from cortical neurons to machine learning and back again. arXiv:1310.​6536.
30.
Zurück zum Zitat Menon AK, Elkan C. Fast algorithms for approximating the singular value decomposition. ACM Trans Knowl Discov Data (TKDD). 2011;5(2):13. Menon AK, Elkan C. Fast algorithms for approximating the singular value decomposition. ACM Trans Knowl Discov Data (TKDD). 2011;5(2):13.
31.
Zurück zum Zitat Lee H, Grosse R, Ranganath R, Ng AY. Unsupervised learning of hierarchical representations with convolutional deep belief networks. Commun ACM. 2011;54(10):95–103.CrossRef Lee H, Grosse R, Ranganath R, Ng AY. Unsupervised learning of hierarchical representations with convolutional deep belief networks. Commun ACM. 2011;54(10):95–103.CrossRef
32.
Zurück zum Zitat Bingham E, Mannila H. Random projection in dimensionality reduction: applications to image and text data. In: ACM SIGKDD. 2001. p. 245–250. Bingham E, Mannila H. Random projection in dimensionality reduction: applications to image and text data. In: ACM SIGKDD. 2001. p. 245–250.
33.
Zurück zum Zitat Sarlos T. Improved approximation algorithms for large matrices via random projections. In: FOCS. 2006. p. 143–152. Sarlos T. Improved approximation algorithms for large matrices via random projections. In: FOCS. 2006. p. 143–152.
34.
Zurück zum Zitat Achlioptas D. Database-friendly random projections: Johnson-Lindenstrauss with binary coins. J Comput Syst Sci. 2003;66(4):671–687.CrossRef Achlioptas D. Database-friendly random projections: Johnson-Lindenstrauss with binary coins. J Comput Syst Sci. 2003;66(4):671–687.CrossRef
35.
Zurück zum Zitat Yichao L, Dhillon P, Foster DP, Ungar L. Faster ridge regression via the subsampled randomized hadamard transform. In: Advances in neural information processing systems. 2013. p. 369–377. Yichao L, Dhillon P, Foster DP, Ungar L. Faster ridge regression via the subsampled randomized hadamard transform. In: Advances in neural information processing systems. 2013. p. 369–377.
36.
Zurück zum Zitat Tropp JA. Improved analysis of the subsampled randomized hadamard transform. Adv Adapt Data Anal. 2011;3(01n02):115–26.CrossRef Tropp JA. Improved analysis of the subsampled randomized hadamard transform. Adv Adapt Data Anal. 2011;3(01n02):115–26.CrossRef
37.
Zurück zum Zitat Lewis L. 1994. Randomness and nondeterminism. In: International congress of mathematicians. Zurich. Lewis L. 1994. Randomness and nondeterminism. In: International congress of mathematicians. Zurich.
38.
Zurück zum Zitat Kolmogorov A, Uspenskii V. Algorithms and randomness. Theor Veroyatnost i Primenen. 1987;3(32):389–412. Kolmogorov A, Uspenskii V. Algorithms and randomness. Theor Veroyatnost i Primenen. 1987;3(32):389–412.
39.
Zurück zum Zitat Jiao L, Denoeux T, Pan Q. A hybrid belief rule-based classification system based on uncertain training data and expert knowledge. IEEE Trans Syst Man Cybern Syst. 2016;46(12):1711–23.CrossRef Jiao L, Denoeux T, Pan Q. A hybrid belief rule-based classification system based on uncertain training data and expert knowledge. IEEE Trans Syst Man Cybern Syst. 2016;46(12):1711–23.CrossRef
40.
Zurück zum Zitat Cambria E, Huang G-B, et al. Extreme learning machines. IEEE Intell Syst. 2013;28(6):30–59.CrossRef Cambria E, Huang G-B, et al. Extreme learning machines. IEEE Intell Syst. 2013;28(6):30–59.CrossRef
41.
Zurück zum Zitat Huang G-B, Zhu Q-Y, Siew C-K. Extreme learning machine: theory and applications. Neurocomputing. 2006;70(1):489–501.CrossRef Huang G-B, Zhu Q-Y, Siew C-K. Extreme learning machine: theory and applications. Neurocomputing. 2006;70(1):489–501.CrossRef
42.
Zurück zum Zitat Huang G-B, Cambria E, Toh K-A, Widrow B, Zongben X. New trends of learning in computational intelligence. IEEE Comput Intell Mag. 2015;10(2):16–7.CrossRef Huang G-B, Cambria E, Toh K-A, Widrow B, Zongben X. New trends of learning in computational intelligence. IEEE Comput Intell Mag. 2015;10(2):16–7.CrossRef
43.
Zurück zum Zitat Oneto L, Bisio F, Cambria E, Anguita D. Statistical learning theory and ELM for big social data analysis. IEEE Comput Intell Mag. 2016;11(3):45–55.CrossRef Oneto L, Bisio F, Cambria E, Anguita D. Statistical learning theory and ELM for big social data analysis. IEEE Comput Intell Mag. 2016;11(3):45–55.CrossRef
44.
Zurück zum Zitat Oneto L, Bisio F, Cambria E, Anguita D. Semi-supervised learning for affective common-sense reasoning. Cogn Comput. 2017;9(1):18–42.CrossRef Oneto L, Bisio F, Cambria E, Anguita D. Semi-supervised learning for affective common-sense reasoning. Cogn Comput. 2017;9(1):18–42.CrossRef
45.
Zurück zum Zitat Huang G-B. An insight into extreme learning machines: random neurons, random features and kernels. Cogn Comput. 2014;6(3):376–90.CrossRef Huang G-B. An insight into extreme learning machines: random neurons, random features and kernels. Cogn Comput. 2014;6(3):376–90.CrossRef
46.
Zurück zum Zitat Ridella S, Rovetta S, Zunino R. Circular backpropagation networks for classification. IEEE Trans Neural Netw. 1997;8(1):84–97.CrossRefPubMed Ridella S, Rovetta S, Zunino R. Circular backpropagation networks for classification. IEEE Trans Neural Netw. 1997;8(1):84–97.CrossRefPubMed
47.
Zurück zum Zitat Vogl TP, Mangis JK, Rigler AK, Zink WT, Alkon DL. Accelerating the convergence of the back-propagation method. Biol Cybern. 1988;59(4-5):257–63.CrossRef Vogl TP, Mangis JK, Rigler AK, Zink WT, Alkon DL. Accelerating the convergence of the back-propagation method. Biol Cybern. 1988;59(4-5):257–63.CrossRef
48.
Zurück zum Zitat Huang G-B, Chen L, Siew C-K. Universal approximation using incremental constructive feedforward networks with random hidden nodes. IEEE Trans Neural Netw. 2006;17(4):879–92.CrossRefPubMed Huang G-B, Chen L, Siew C-K. Universal approximation using incremental constructive feedforward networks with random hidden nodes. IEEE Trans Neural Netw. 2006;17(4):879–92.CrossRefPubMed
49.
Zurück zum Zitat Huang G-B, Wang DH, Lan Y. Extreme learning machines: a survey. Int J Mach Learn Cybern. 2011;2(2):107–122.CrossRef Huang G-B, Wang DH, Lan Y. Extreme learning machines: a survey. Int J Mach Learn Cybern. 2011;2(2):107–122.CrossRef
50.
Zurück zum Zitat Huang G-B, Zhou H, Ding X, Zhang R. Extreme learning machine for regression and multiclass classification. IEEE Trans Syst Man Cybern Part B: Cybern. 2012;42(2):513–29.CrossRef Huang G-B, Zhou H, Ding X, Zhang R. Extreme learning machine for regression and multiclass classification. IEEE Trans Syst Man Cybern Part B: Cybern. 2012;42(2):513–29.CrossRef
51.
Zurück zum Zitat Dyer M. Connectionist natural language processing: a status report, volume 292 of Computational architectures integrating neural and symbolic processes. Dordrecht: Kluwer Academic; 1995, pp. 389–429. Dyer M. Connectionist natural language processing: a status report, volume 292 of Computational architectures integrating neural and symbolic processes. Dordrecht: Kluwer Academic; 1995, pp. 389–429.
52.
Zurück zum Zitat Cambria E, White B. Jumping NLP curves: a review of natural language processing research. IEEE Comput Intell Mag. 2014;9(2):48–57.CrossRef Cambria E, White B. Jumping NLP curves: a review of natural language processing research. IEEE Comput Intell Mag. 2014;9(2):48–57.CrossRef
53.
Zurück zum Zitat Chaturvedi I, Ong Y-S, Tsang IW, Welsch RE, Cambria E. Learning word dependencies in text by means of a deep recurrent belief network. Knowl-Based Syst. 2016;108:144–54.CrossRef Chaturvedi I, Ong Y-S, Tsang IW, Welsch RE, Cambria E. Learning word dependencies in text by means of a deep recurrent belief network. Knowl-Based Syst. 2016;108:144–54.CrossRef
54.
Zurück zum Zitat Rowley J. The wisdom hierarchy: representations of the DIKW hierarchy. J Inf Sci. 2007;33(2):163–180.CrossRef Rowley J. The wisdom hierarchy: representations of the DIKW hierarchy. J Inf Sci. 2007;33(2):163–180.CrossRef
55.
Zurück zum Zitat Cambria E, Wang H, White B. Guest editorial: big social data analysis. Knowl-Based Syst. 2014;69:1–2.CrossRef Cambria E, Wang H, White B. Guest editorial: big social data analysis. Knowl-Based Syst. 2014;69:1–2.CrossRef
56.
Zurück zum Zitat Cambria E, Hussain A. Sentic computing: a common-sense-based framework for concept-level sentiment analysis. Cham: Springer; 2015.CrossRef Cambria E, Hussain A. Sentic computing: a common-sense-based framework for concept-level sentiment analysis. Cham: Springer; 2015.CrossRef
57.
Zurück zum Zitat Poria S, Cambria E, Gelbukh A, Bisio F, Hussain A. Sentiment data flow analysis by means of dynamic linguistic patterns. IEEE Comput Intell Mag. 2015;10(4):26–36. Poria S, Cambria E, Gelbukh A, Bisio F, Hussain A. Sentiment data flow analysis by means of dynamic linguistic patterns. IEEE Comput Intell Mag. 2015;10(4):26–36.
58.
Zurück zum Zitat Cambria E, Poria S, Bajpai R, Schuller B. SenticNet 4: a semantic resource for sentiment analysis based on conceptual primitives. In: COLING. 2016. p. 2666–2677. Cambria E, Poria S, Bajpai R, Schuller B. SenticNet 4: a semantic resource for sentiment analysis based on conceptual primitives. In: COLING. 2016. p. 2666–2677.
59.
Zurück zum Zitat Cambria E, Hussain A, Havasi C, Eckl C, Munro J. 2010. Towards crowd validation of the UK national health service. In: WebSci. Raleigh. Cambria E, Hussain A, Havasi C, Eckl C, Munro J. 2010. Towards crowd validation of the UK national health service. In: WebSci. Raleigh.
61.
Zurück zum Zitat Menzel S, Linn E, Waser R. Redox-based resistive memory. Wiley; 2015. vol. 1, chapter 8, p. 137–161. Menzel S, Linn E, Waser R. Redox-based resistive memory. Wiley; 2015. vol. 1, chapter 8, p. 137–161.
62.
Zurück zum Zitat Valov I, Tappertzhofen S, Linn E, Menzel S, van den Hurk J, Waser R. Atomic scale and interface interactions in redox-based resistive switching memories. ECS Trans. 2014;64(14):3–18.CrossRef Valov I, Tappertzhofen S, Linn E, Menzel S, van den Hurk J, Waser R. Atomic scale and interface interactions in redox-based resistive switching memories. ECS Trans. 2014;64(14):3–18.CrossRef
63.
Zurück zum Zitat Zhirnov VV, Meade R, Cavin RK, Sandhu G. Scaling limits of resistive memories. Nanotechnology. 2011;22(25):254027/1–21.CrossRef Zhirnov VV, Meade R, Cavin RK, Sandhu G. Scaling limits of resistive memories. Nanotechnology. 2011;22(25):254027/1–21.CrossRef
64.
Zurück zum Zitat Chien W-C, Lee M-H, Lee F-M, Lin Y-Y, Lung H-L, Hsieh K-Y, Lu C-Y. A multi-level 40nm WOX resistive memory with excellent reliability. In: 2011 IEEE international electron devices meeting IEDM ’11. 2011. Chien W-C, Lee M-H, Lee F-M, Lin Y-Y, Lung H-L, Hsieh K-Y, Lu C-Y. A multi-level 40nm WOX resistive memory with excellent reliability. In: 2011 IEEE international electron devices meeting IEDM ’11. 2011.
65.
Zurück zum Zitat Kügeler C, Meier M, Rosezin R, Gilles S, Waser R. High density 3D memory architecture based on the resistive switching effect. Solid State Electron. 2009;53(12):1287–92.CrossRef Kügeler C, Meier M, Rosezin R, Gilles S, Waser R. High density 3D memory architecture based on the resistive switching effect. Solid State Electron. 2009;53(12):1287–92.CrossRef
67.
Zurück zum Zitat Strukov DB, Snider GS, Stewart DR, Williams RS. The missing memristor found. Nature. 2008;453(7191):80–3.CrossRefPubMed Strukov DB, Snider GS, Stewart DR, Williams RS. The missing memristor found. Nature. 2008;453(7191):80–3.CrossRefPubMed
68.
Zurück zum Zitat Chua LO, Kang SM. Memristive devices and systems. Proc IEEE. 1976;64(2):209–23.CrossRef Chua LO, Kang SM. Memristive devices and systems. Proc IEEE. 1976;64(2):209–23.CrossRef
69.
Zurück zum Zitat Borghetti J, Snider GS, Kuekes PJ, Yang JJ, Stewart DR, Williams RS. ‘Memristive’ switches enable ‘stateful’ logic operations via material implication. Nature. 2010;464(7290):873–76.CrossRefPubMed Borghetti J, Snider GS, Kuekes PJ, Yang JJ, Stewart DR, Williams RS. ‘Memristive’ switches enable ‘stateful’ logic operations via material implication. Nature. 2010;464(7290):873–76.CrossRefPubMed
70.
Zurück zum Zitat Linn E, Rosezin R, Tappertzhofen S, Böttger U, Waser R. Beyond von Neumann—logic operations in passive crossbar arrays alongside memory operations. Nanotechnology. 2012;23:305205.CrossRefPubMed Linn E, Rosezin R, Tappertzhofen S, Böttger U, Waser R. Beyond von Neumann—logic operations in passive crossbar arrays alongside memory operations. Nanotechnology. 2012;23:305205.CrossRefPubMed
71.
Zurück zum Zitat Kim W, Chattopadhyay A, Siemon A, Linn E, Waser R, Rana V. Multistate memristive tantalum oxide devices for ternary arithmetic. Sci Rep 2016;6:36652 EP –, 11.CrossRef Kim W, Chattopadhyay A, Siemon A, Linn E, Waser R, Rana V. Multistate memristive tantalum oxide devices for ternary arithmetic. Sci Rep 2016;6:36652 EP –, 11.CrossRef
72.
Zurück zum Zitat Siemon A, Breuer T, Aslam N, Ferch S, Kim W, van den Hurk J, Rana V, Hoffmann-Eifert S, Waser R, Menzel S, Linn E. 2015. Realization of Boolean logic functionality using redox-based memristive devices. Adv Funct Mater. Siemon A, Breuer T, Aslam N, Ferch S, Kim W, van den Hurk J, Rana V, Hoffmann-Eifert S, Waser R, Menzel S, Linn E. 2015. Realization of Boolean logic functionality using redox-based memristive devices. Adv Funct Mater.
73.
Zurück zum Zitat Siemon A, Menzel S, Chattopadhyay A, Waser R, Linn E. 2015. In-memory adder functionality in 1S1R arrays. In: Proceedings of 2014 IEEE international symposium on circuits and systems (ISCAS). Siemon A, Menzel S, Chattopadhyay A, Waser R, Linn E. 2015. In-memory adder functionality in 1S1R arrays. In: Proceedings of 2014 IEEE international symposium on circuits and systems (ISCAS).
74.
Zurück zum Zitat Siemon A, Menzel S, Waser R, Linn E. A complementary resistive switch-based crossbar array adder. IEEE J Emerg Sel Top Circ Syst. 2015;5(1):64–74.CrossRef Siemon A, Menzel S, Waser R, Linn E. A complementary resistive switch-based crossbar array adder. IEEE J Emerg Sel Top Circ Syst. 2015;5(1):64–74.CrossRef
75.
Zurück zum Zitat Breuer T, Siemon A, Linn E, Menzel S, Waser R, Rana V. 2015. A HfO2-based complementary switching crossbar adder. Adv Electron Mater. Breuer T, Siemon A, Linn E, Menzel S, Waser R, Rana V. 2015. A HfO2-based complementary switching crossbar adder. Adv Electron Mater.
76.
Zurück zum Zitat Bhattacharjee D, Chattopadhyay A. Efficient binary basic linear algebra operations on reram crossbar arrays. In: 2017 30th international conference on VLSI design and 2017 16th international conference on embedded systems (VLSID). 2017. p. 277–282. Bhattacharjee D, Chattopadhyay A. Efficient binary basic linear algebra operations on reram crossbar arrays. In: 2017 30th international conference on VLSI design and 2017 16th international conference on embedded systems (VLSID). 2017. p. 277–282.
77.
Zurück zum Zitat Bhattacharjee D, Chattopadhyay A. In-memory data compression using ReRAMs. Springer International Publishing; 2017. p. 275–291. Bhattacharjee D, Chattopadhyay A. In-memory data compression using ReRAMs. Springer International Publishing; 2017. p. 275–291.
78.
82.
Zurück zum Zitat Bekenstein JD. Universal upper bound on the entropy-to-energy ratio for bounded systems. Phys Rev D. 1981; 23(2):287–98.CrossRef Bekenstein JD. Universal upper bound on the entropy-to-energy ratio for bounded systems. Phys Rev D. 1981; 23(2):287–98.CrossRef
83.
Zurück zum Zitat Church GM, Gao Y, Kosuri S. Next-generation digital information storage in DNA. Science. 2012;337(6102):1628.CrossRefPubMed Church GM, Gao Y, Kosuri S. Next-generation digital information storage in DNA. Science. 2012;337(6102):1628.CrossRefPubMed
85.
Zurück zum Zitat Higgins S. The DCC curation lifecycle model. Int J Digit Curat. 2008;3(1):134–40.CrossRef Higgins S. The DCC curation lifecycle model. Int J Digit Curat. 2008;3(1):134–40.CrossRef
86.
Zurück zum Zitat Klein G, Calderwood R, MacGregor D. Critical decision method for eliciting knowledge. IEEE Trans Syst Man Cybern. 2002;19(3):462–72.CrossRef Klein G, Calderwood R, MacGregor D. Critical decision method for eliciting knowledge. IEEE Trans Syst Man Cybern. 2002;19(3):462–72.CrossRef
Metadaten
Titel
Storages Are Not Forever
verfasst von
Erik Cambria
Anupam Chattopadhyay
Eike Linn
Bappaditya Mandal
Bebo White
Publikationsdatum
27.05.2017
Verlag
Springer US
Erschienen in
Cognitive Computation / Ausgabe 5/2017
Print ISSN: 1866-9956
Elektronische ISSN: 1866-9964
DOI
https://doi.org/10.1007/s12559-017-9482-4

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