Skip to main content
×
×
Home

AI, native supercomputing and the revival of Moore's Law

  • Chien-Ping Lu (a1)
Abstract

Artificial Intelligence (AI) was the inspiration that shaped computing as we know it today. In this article, I explore why and how AI would continue to inspire computing and reinvent it when Moore's Law is running out of steam. At the dawn of computing, Alan Turing proposed that instead of comprising many different specific machines, the computing machinery for AI should be a Universal Digital Computer, modeled after human computers, which carry out calculations with pencil on paper. Based on the belief that a digital computer would be significantly faster, more diligent and patient than a human, he anticipated that AI would be advanced as software. In modern terminology, a universal computer would be designed to understand a language known as an Instruction Set Architecture (ISA), and software would be translated into the ISA. Since then, universal computers have become exponentially faster and more energy efficient through Moore's Law, while software has grown more sophisticated. Even though software has not yet made a machine think, it has been changing how we live fundamentally. The computing revolution started when the software was decoupled from the computing machinery. Since the slowdown of Moore's Law in 2005, the universal computer is no longer improving exponentially in terms of speed and energy efficiency. It has to carry ISA legacy, and cannot be aggressively modified to save energy. Turing's proposition of AI as software is challenged, and the temptation of making many domain-specific AI machines emerges. Thanks to Deep Learning, software can stay decoupled from the computing machinery in the language of linear algebra, which it has in common with supercomputing. A new universal computer for AI understands such language natively to then become a Native Supercomputer. AI has been and will still be the inspiration for computing. The quest to make machines think continues amid the slowdown of Moore's Law. AI might not only maximize the remaining benefits of Moore's Law, but also revive Moore's Law beyond current technology.

  • View HTML
    • Send article to Kindle

      To send this article to your Kindle, first ensure no-reply@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about sending to your Kindle. Find out more about sending to your Kindle.

      Note you can select to send to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be sent to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

      Find out more about the Kindle Personal Document Service.

      AI, native supercomputing and the revival of Moore's Law
      Available formats
      ×
      Send article to Dropbox

      To send this article to your Dropbox account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Dropbox.

      AI, native supercomputing and the revival of Moore's Law
      Available formats
      ×
      Send article to Google Drive

      To send this article to your Google Drive account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Google Drive.

      AI, native supercomputing and the revival of Moore's Law
      Available formats
      ×
Copyright
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Corresponding author
Corresponding author: C.-P. Lu Email: cpl@novumind.com
References
Hide All
[1] Turing, A.: Computing machinery and intelligence. Mind, 50 (1950), 433460.
[2] Turing, A.: On computable numbers, with an application to the Entscheidungsproblem. Proc. London Math. Soc. Ser. 2 42 (1936), 230265.
[3] Turing, A.: Programmers’ Handbook for the Manchester Electronic Computer, Manchester University, Manchester, England, 1950.
[4] Neumann, J.v.: First Draft of a Report on the EDVAC. 1945. [Online]. Available: https://sites.google.com/site/michaeldgodfrey/vonneumann/vnedvac.pdf?attredirects=0&d=1.
[5] Moore, G.: Cramming more components onto integrated circuits. Electronics, 8 (1965), 8285.
[6] Sutter, H.: The Free Lunch is Over: A Fundamental Turn Toward Concurrency in Software. 2005. [Online]. Available: http://www.gotw.ca/publications/concurrency-ddj.htm.
[7] Esmaeilzadeh, H.; Blem, E.; St. Amant, R.; Sankaralingam, K.; Burger, D.: Dark silicon and the end of multicore scaling, in The 38th International Symposium on Computer Architecture (ISCA), 2011, 365376.
[8] Hodges, A.: Alan Turing. 30 Sep 2013. [Online]. Available: https://plato.stanford.edu/entries/turing/#Unc.
[9] BLAS (Basic Linear Algebra Subprograms). [Online]. Available: http://www.netlib.org/blas/.
[10] Cappello, F.; Guermouche, A.; Snir, M.: On communication determinism in parallel HPC applications, in Int. Conf. on Computer Communication Networks, Zurich, Switzerland, 2010.
[11] Chetlur, S.; Woolley, C.; Vandermersch, P.; Cohen, J.; Tran, J.; Catanzaro, B.; Shelhamer, E.: DNN: Efficient Primitives for Deep Learning, 18 Dec 2014. [Online]. Available: https://arxiv.org/abs/1410.0759.
[12] Jouppi, N.P.: In-Datacenter Performance Analysis of a Tensor Processing Unit, Google, Inc, 2017. [Online]. Available: https://drive.google.com/file/d/0Bx4hafXDDq2EMzRNcy1vSUxtcEk/view.
[13] Kung, H.T.: Why systolic architecture? IEEE Comput., 15 (1) (1982), 3746.
[14] Lu, C.-P.: Should We All Embrace Systolic Arrays? 28 April 2017. [Online]. Available: https://www.linkedin.com/pulse/should-we-all-embrace-systolic-arrays-chien-ping-lu.
[15] Luo, T.; Liu, S.; Li, L.; Wang, Y.; Zhang, S.; Chen, T.; Xu, A.; Temam, O.; Chen, Y.: DaDianNao: a neural network supercomputer. IEEE Trans. Comput. 66 ( 2017), 7388.
[16] Sze, V.: Efficient Processing of Deep Neural Networks: A Tutorial and Survey. 27 Mar 2017. [Online]. Available: https://arxiv.org/abs/1703.09039.
[17] Fisher, D.C.: Your favorite parallel algorithms might not be as fast as you think. IEEE Trans. Comput., 37 (2) (1988), 211213.
[18] Robert, A. van de Geijn, J.W.: SUMMA: Scalable Universal Matrix Multiplication Algorithm. Technical Report UT CS-95-28, pp. 255274, Vol. 9. Department of Computer Science, The University of Texas at Austin, 1997.
[19] Thakur, R.; Rabenseifner, R.; Gropp, W.: Optimization of Collective Communication Operations in MPICH. 2005. [Online]. Available: http://www.mcs.anl.gov/~thakur/papers/ijhpca-coll.pdf.
[20] Brooks, E.: The attack of killer micros, in Supercomputing 1989, Reno, NV, 1989.
Recommend this journal

Email your librarian or administrator to recommend adding this journal to your organisation's collection.

APSIPA Transactions on Signal and Information Processing
  • ISSN: 2048-7703
  • EISSN: 2048-7703
  • URL: /core/journals/apsipa-transactions-on-signal-and-information-processing
Please enter your name
Please enter a valid email address
Who would you like to send this to? *
×

Keywords

Metrics

Full text views

Total number of HTML views: 80
Total number of PDF views: 177 *
Loading metrics...

Abstract views

Total abstract views: 285 *
Loading metrics...

* Views captured on Cambridge Core between 29th August 2017 - 17th August 2018. This data will be updated every 24 hours.