Sara Hooker
Skip Abstract Section
Abstract
After decades of incentivizing the isolation of hardware, software, and algorithm development, the catalysts for closer collaboration are changing the paradigm.
- Amodei, D., Hernandez, D., Sastry, G., Clark, J., Brockman, G., and Sutskever, I. AI and compute. OpenAI (2018), https://openai.com/blog/ai-and-compute/.Google Scholar
- ARM. Enhancing AI performance for IoT endpoint devices. (2020), https://www.arm.com/company/news/2020/02/new-ai-technology-from-armGoogle Scholar
- Barham, P. and Isard, M. Machine learning systems are stuck in a rut. In Proceedings of the Workshop on Hot Topics in Operating Systems (HotOS '19), (Bertinoro, Italy), ACM, New York, NY, USA, 177--183. Google ScholarDigital Library
- Barnett, S. and Ceci, S. When and where do we apply what we learn? A taxonomy for far transfer. Psychological Bulletin 128, 4 (2002), 612--37.Google ScholarCross Ref
- Bi, G. and Poo, M. Synaptic modifications in cultured hippocampal neurons: Dependence on spike timing, synaptic strength, and postsynaptic cell type. Journal of Neuroscience 18, 24 (1998), 10464--10472. arXiv:https://www.jneurosci.org/content/18/24/10464.full.pdf Google ScholarCross Ref
- Bubic, A., Cramon, D., and Schubotz, R. Prediction, cognition, and the brain. Frontiers in Human Neuroscience 4 (2010), 25. Google ScholarCross Ref
- Chellapilla, K., Puri, S., and Simard, P. High performance convolutional neural networks for document processing. Tenth International Workshop on Frontiers in Handwriting Recognition (2006).Google Scholar
- Coates, A., Huval, B., Wang, T., Wu, D., Catanzaro, B., and Andrew, N. Deep learning with COTS HPC systems. In Proceedings of the 30th Intern. Conf. on Machine Learning (2013), Sanjoy Dasgupta and David McAllester (Eds.). PMLR, Atlanta, GA, USA, 1337--1345. http://proceedings.mlr.press/v28/coates13.htmlGoogle Scholar
- Collier, B. Little engines that could've: The calculating machines of Charles Babbage. Garland Publishing, Inc. (1991) USA.Google Scholar
- Computer history 1949--1960: Early vacuum tube computers overview. Computer History Archives Project (2018), https://www.youtube.com/watch?v=WnNm_uJYWhAGoogle Scholar
- Dean, J. The deep learning revolution and its implications for computer architecture and chip design. IEEE International Solid-State Circuits Conference (2020), 8--14.Google Scholar
- Dennard, R., Gaensslen, F., Yu, H., Rideout, V., Bassous, E., and LeBlanc, A. Design of ion implanted MOSFET's with very small physical dimensions. IEEE Journal of Solid-State Circuits 9, 5 (1974), 256--268.Google ScholarCross Ref
- Dongarra, J., Gates, M., Kurzak, J., Luszczek, P., and Tsai, Y. Autotuning numerical dense linear algebra for batched computation with GPU hardware accelerators. In Proceedings of the IEEE 106, 11 (2018), 2040--2055.Google ScholarCross Ref
- Feldman, M. The era of general-purpose computers is ending. The Next Platform (2019), https://bit.ly/3hP8XJhGoogle Scholar
- Fukushima, K. and Miyake, S. Neocognitron: A new algorithm for pattern recognition tolerant of deformations and shifts in position. Pattern Recognition 15, 6 (1982), 455--469. http://www.sciencedirect.com/science/article/pii/0031320382900243Google ScholarCross Ref
- Gupta, S. and Tan, M. EfficientNet-Edge TPU: Creating accelerator-optimized neural networks with AutoML. Google AI Blog (2019), https://ai.googleblog.com/2019/08/efficientnet-edgetpu-creating.htmlGoogle Scholar
- Hauck, S. and DeHon, A. Reconfigurable Computing: The Theory and Practice of FPGA-Based Computation. (2017), Morgan Kaufmann Publishers Inc., San Francisco, CA, USA.Google Scholar
- Herculano-Houzel, S., et al. The elephant brain in numbers. Frontiers in Neuroanatomy 8 (2014).Google Scholar
- Hinton, G. and Anderson, J. Parallel Models of Associative Memory. (1989), L. Erlbaum Associates Inc., USA.Google Scholar
- Horowitz, M. Computing's energy problem (and what we can do about it). In 2014 IEEE International Solid-State Circuits Conference Digest of Technical Papers (ISSCC). 10--14.Google Scholar
- Hotel, H., Johansen, H., Bernholdt, D., Héroux, M., and Hornung, R. Software productivity for extreme-scale science. U.S. Department of Energy Advanced Scientific Computing Research (2014).Google Scholar
- Isaacson, W. Grace Hopper, computing pioneer. The Harvard Gazette (2014). https://news.harvard.edu/gazette/story/2014/12/grace-hopper-computing-pioneer/Google Scholar
- Jouppi, N., et al. In-datacenter performance analysis of a tensor processing unit. SIGARCH Comput. Archit. News 45, 2 (June 2017), 1--12. Google ScholarDigital Library
- Kuhn, T. The Structure of Scientific Revolutions. (1962), University of Chicago Press, Chicago.Google Scholar
- Kurzweil, R. The Age of Intelligent Machines. (1990), MIT Press, Cambridge, MA, USA.Google Scholar
- Larus, J. Spending Moore's dividend. Commun. ACM 52, 5 (May 2009), 62--69. Google ScholarDigital Library
- LeCun, Y., Boser, B., Denker, J., Henderson, D., Howard, R., Hubbard, W., and Jackel, L. Backpropagation applied to handwritten zip code recognition. Neural Computation 1, 4 (1989), 541--551. Google ScholarDigital Library
- Lee, H., Brown, K., Sujeeth, A., Chafi, H., Rompf, T., Odersky, M., and Olukotun, K. Implementing domain-specific languages for heterogeneous parallel computing. IEEE Micro 31, 5 (2011), 42--53.Google ScholarDigital Library
- Linnainmaa, S. Taylor expansion of the accumulated rounding error. BIT Numerical Mathematics 16 (1976), 146--160.Google ScholarDigital Library
- McClelland, J., McNaughton, B., and O'Reilly, R. Why there are complementary learning systems in the hippocampus and neocortex: Insights from the successes and failures of connectionist models of learning and memory. Psychological Review 102 (Aug. 1995), 419--57. Google ScholarCross Ref
- Mirhoseini, A., et al. A graph placement methodology for fast chip design. Nature 594 (June 9, 2021), 207--212, https://www.nature.com/articles/s41586-021-03544-w.Google ScholarCross Ref
- Moore, D. The Anna Karenina Principle applied to ecological risk assessments of multiple stressors. Human and Ecological Risk Assessment: An International Journal 7, 2 (2001), 231--237. Google ScholarCross Ref
- Moore, G. 1965. Cramming more components onto integrated circuits. Electronics 38, 8 (April 1965). https://www.cs.utexas.edu/~fussell/courses/cs352h/papers/moore.pdfGoogle Scholar
- Moravec, H. When will computer hardware match the human brain. Journal of Transhumanism 1 (1998).Google Scholar
- Olukotun, K. Beyond parallel programming with domain specific languages. SIGPLAN Not. 49, 8 (Feb. 2014), 179--180. Google ScholarDigital Library
- Posselt, E.A. The Jacquard Machine Analyzed and Explained: The Preparation of Jacquard Cards and Practical Hints to Learners of Jacquard Designing. (1888).Google Scholar
- Prabhakar, R., et al. Plasticine: A reconfigurable architecture for parallel patterns. In 2017 ACM/IEEE 44th Annual Intern. Symposium on Computer Architecture (ISCA). 389--402.Google Scholar
- Reddi, V., et al. MLPerf inference benchmark. In 2020 ACM/IEEE 47th Annual Intern. Symposium on Computer Architecture (2020), 446--459.Google ScholarDigital Library
- Rumelhart, D., Hinton, G., and Williams, R. Learning representations by back-propagating errors. MIT Press (1988), 696--699.Google Scholar
- Sabour, S., Frost, N., and Hinton, G. Dynamic routing between capsules. (2017), 3856--3866. http://papers.nips.cc/paper/6975-dynamic-routing-between-capsules.pdfGoogle Scholar
- Shalf, J. The future of computing beyond Moore's law. Philosophical Transactions of the Royal Society A, 378 (2020).Google Scholar
- Singh, V., Perdigones, A., Garcia, J., Cañas, I., and Mazarrón, F. Analyzing worldwide research in hardware architecture, 1997--2011. Commun. ACM 58 (January 2015), 76--85. Google ScholarDigital Library
- Steinbuch, K. and Piske, U.A.W. Learning matrices and their applications. IEEE Transactions on Electronic Computers EC-12, 6 (1963), 846--862.Google ScholarCross Ref
- Stroop, J. Studies of interference in serial verbal reactions. J. of Experimental Psychology 18, 6 (1935), 643. Google ScholarCross Ref
- Thompson, N. and Spanuth, S. The decline of computers as a general purpose technology: Why deep learning and the end of Moore's Law are fragmenting computing. (November 2018).Google Scholar
- Thompson, N., Greenewald, K., Lee, K., and Manso, G. The computational limits of deep learning. arXiv e-prints, Article arXiv:2007.05558 (July 2020), arXiv:2007.05558 pages. arXiv:2007.05558 [cs.LG]Google Scholar
- Tolstoy, L. and Bartlett, R. Anna Karenina. Oxford University Press (2016), https://books.google.com/books?id=1DooDwAAQBAJGoogle ScholarCross Ref
- Van Der Malsburg, C. Frank Rosenblatt: Principles of neurodynamics: Perceptrons and the theory of brain mechanisms. Brain Theory (1986), 245--248.Google ScholarCross Ref
- Von Neumann, J., Churchland, P.M., and Churchland, P.S. The Computer and the Brain. Yale University Press (2000), https://books.google.com/books?id=Q30MqJjRv1gCGoogle Scholar
- Warden, P. and Situnayake, D. TinyML: Machine Learning With TensorFlow Lite on Arduino and Ultra-Low-Power Microcontrollers. (2019), O'Reilly Media, Inc. https://books.google.com/books?id=sB3mxQEACAAJGoogle Scholar
Index Terms
- The hardware lottery
Recommendations
Lottery Pricing Equilibria
EC '16: Proceedings of the 2016 ACM Conference on Economics and ComputationWe extend the notion of Combinatorial Walrasian Equilibrium, as defined by \citet{FGL13}, to settings with budgets. When agents have budgets, the maximum social welfare as traditionally defined is not a suitable benchmark since it is overly optimistic. ...
How many lottery tickets to buy?
We investigate the optimal number of tickets an expected-utility-maximizing individual who participate in a lottery will buy. We show that the expected utility is not always unimodal in the number of tickets. We also show that a risk-averse individual ...
The research and design of an applied electronic lottery system
ICPCA/SWS'12: Proceedings of the 2012 international conference on Pervasive Computing and the Networked WorldAn applied electronic lottery system is designed and constructed in this paper. In the system, electronic lottery has all characteristics which lottery based on paper has. It is a simple, facilitate, shortcut lottery sales system. Lottery buyer can buy ...
Comments