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Issue No.01 - Jan.-June (2012 vol.11)
pp: 1-4
Simha Sethumadhavan , Columbia University, New York
Ryan Roberts , Columbia University, New York
Yannis Tsividis , Columbia University, New York
Current technology trends indicate that power- and energyefficiency will limit chip throughput in the future. Current solutions to these problems, either in the way of programmable or fixed-function digital accelerators will soon reach their limits as microarchitectural overheads are successively trimmed. A significant departure from current computing methods is required to carry forward computing advances beyond digital accelerators. In this paper we describe how the energy-efficiency of a large class of problems can be improved by employing a hybrid of the discrete and continuous models of computation instead of the ubiquitous, traditional discrete model of computation. We present preliminary analysis of domains and benchmarks that can be accelerated with the new model. Analysis shows that machine learning, physics and up to one-third of SPEC, RMS and Berkeley suite of applications can be accelerated with the new hybrid model.
Hybrid systems, Design studies
Simha Sethumadhavan, Ryan Roberts, Yannis Tsividis, "A Case for Hybrid Discrete-Continuous Architectures", IEEE Computer Architecture Letters, vol.11, no. 1, pp. 1-4, Jan.-June 2012, doi:10.1109/L-CA.2011.22
1. R.S. Amant, D.A. Jiménez, and D. Burger, Low-power, high-performance analog neural branch prediction. In Proceedings of the 2008 41st IEEE/ACM International Symposium on Microarchitecture- Volume 00, pages 447-458. IEEE Computer Society Washington, DC, USA, 2008.
2. S. Chakrabartty and G. Cauwenberghs., Sub-microwatt analog VLSI support vector machine for pattern classification and sequence estimation. Adv in Neural Information Processing Systems, 17.
3. G.E.R. Cowan,R.C. Melville,, and Y.P. Tsividis., A vlsi analog computer/digital computer accelerator. Solid-State Circuits, IEEE Journal of, 41(1): 42-53, jan. 2006.
4. C.C. Douglas,J. Mandel,, and W.L. Miranker., Fast hybrid solution of algebraic systems. SIAM J. Sci. Stat. Comput, 11: 1073-1086, 1990.
5. H. Esmaeilzadeh,E. Blem,R.S. Amant,K. Sankaralingam,, and D. Burger., Dark Silicon and the End of Multicore Scaling. In Proceedings of the 38th International Symposium on Computer Architecture, June 2011.
6. H. Franke,J. Xenidis,C. Basso,B. M. Bass,S. S. Woodward,J. D. Brown,, and C. L. Johnson., Introduction to the wire-speed processor and architecture. IBM Journal of Research and Development, 54(1): 3:1-3:11, 2010.
7. R. Hameed,W. Qadeer,M. Wachs,O. Azizi,A. Solomatnikov,B. C. Lee,S. Richardson,C. Kozyrakis,, and M. Horowitz., Understanding sources of inefficiency in general-purpose chips. In ISCA, pages 37-47, 2010.
8. Jason Harrison,Ronald A. Rensink,, and Michiel van de Panne., Obscuring length changes during animated motion. ACM Trans. Graph., 23(3): 569-573, 2004.
9. WJ Karplus and RA Russell., Increasing digital computer efficiency with the aid of error-correcting analog subroutines. IEEE Transactions on Computers, 100(20): 831-837, 1971.
10. G.A. Korn., The impact of hybrid analog-digital techniques on the analog-computer art. Proceedings of the IRE, 50(5): 1077-1086, May 1962.
11. G.A. Korn and T.M. Korn., Electronic analog and hybrid computers. McGraw-Hill New York, 1972.
12. Carver Mead. Analog VLSI and Neural Systems. Addison-Wesley Longman Publishing Co., Inc., Boston, MA, USA, 1989.
13. S.Y. Peng,B.A. Minch,, and P. Hasler., Analog VLSI implementation of support vector machine learning and classification. In IEEE International Symposium on Circuits and Systems, 2008. ISCAS 2008, pages 860-863, 2008.
14. A.I. Rubin and J.B. Mawson., Hybrid computation 1976 and its future. Computer, 9(7): 37-46, july 1976.
15. J. F. Traub., A continuous model of computation. Physics Today, page 39, 1999.
16. Thomas Y. Yeh,Petros Faloutsos,Sanjay J. Patel,, and Glenn Reinman., Parallax: an architecture for real-time physics. SIGARCH Comput. Archit. News, 35(2): 232-243, 2007.
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