Fetching the paper…
Reading the bibliography…
Hardware, systems and algorithms research communities have historically had different incentive structures and fluctuating motivation to engage with each other explicitly.
Studies of interference in serial verbal reactions
Stroop, J. R · 1935
Earlier work this paper cites.
The Structure of Scientific Revolutions
Kuhn, T. S · 1962
Earlier work this paper cites.
Learning matrices and their applications
K, S. and Piske, U · 1963
Earlier work this paper cites.
Cramming more components onto integrated circuits
Moore, G · 1965
Earlier work this paper cites.
Design of ion-implanted mosfet’s with very small physical dimensions
Dennard, R. H., Gaensslen, F. H., Yu, H., Rideout, V. L., Bassous, E., and LeBlanc, A. R · 1974
Earlier work this paper cites.
Taylor expansion of the accumulated rounding error
Linnainmaa, S · 1976
Earlier work this paper cites.
Neocognitron: A new algorithm for pattern recognition tolerant of deformations and shifts in position
Fukushima, K. and Miyake, S · 1982
Earlier work this paper cites.
The fifth generation: Artificial intelligence and japan’s computer challenge to the world, by edward a. feigenbaum and pamela mccorduck. reading, ma: Addison-wesley, 1983, 275 pp. price: $15.35
Morgan, M. G · 1983
Earlier work this paper cites.
Artificial Intelligence: The Very Idea
Haugeland, J · 1985
Earlier work this paper cites.
Understanding computers: software
Time · 1985
Earlier work this paper cites.
Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Vol. 1: Foundations
Rumelhart, D. E., McClelland, J. L., and PDP Research Group, C. (eds.) · 1986
Earlier work this paper cites.
Frank rosenblatt: Principles of neurodynamics: Perceptrons and the theory of brain mechanisms, 1986
Van Der Malsburg, C · 1986
Earlier work this paper cites.
Learning Representations by Back-Propagating Errors , pp. 696–699
Rumelhart, D. E., Hinton, G. E., and Williams, R. J · 1988
Earlier work this paper cites.
Parallel Models of Associative Memory
Hinton, G. E. and Anderson, J. A · 1989
Earlier work this paper cites.
Backpropagation applied to handwritten zip code recognition, 1989
LeCun, Y., Boser, B., Denker, J. S., Henderson, D., Howard, R. E., Hubbard, W., and Jackel, L. D · 1989
Earlier work this paper cites.
Catastrophic interference in connectionist networks: The sequential learning problem, 1989
McCloskey, M. and Cohen, N. J · 1989
Earlier work this paper cites.
Parallel implementations of neural network training: two back-propagation approaches., 1990
Dean, J · 1990
Earlier work this paper cites.
The Age of Intelligent Machines
Kurzweil, R · 1990
Earlier work this paper cites.
Little Engines That Could’ve: The Calculating Machines of Charles Babbage
Collier, B · 1991
Earlier work this paper cites.
Principles of expert systems, 1991
Lucas, P. and van der Gaag, L · 1991
Earlier work this paper cites.
The ring array processor: A multiprocessing peripheral for connectionist applications
Morgan, N., Beck, J., Kohn, P., Bilmes, J., Allman, E., and Beer, J · 1992
Earlier work this paper cites.
Application of the anna neural network chip to high-speed character recognition
Sackinger, E., Boser, B. E., Bromley, J., LeCun, Y., and Jackel, L. D · 1992
Earlier work this paper cites.
Neural network toolbox for use with matlab - user guide verion 3.0, 1993
Demuth, H. and Beale, M · 1993
Earlier work this paper cites.
SPACE: Symbolic Processing in Associative Computing Elements , pp. 243–252
Howe, D. B. and Asanović, K · 1994
Earlier work this paper cites.
Review of hardware neural networks: A User’s perspective
Lindsey, C. S. and Lindblad, T · 1994
Earlier work this paper cites.
Why there are complementary learning systems in the hippocampus and neocortex: Insights from the successes and failures of connectionist models of learning and memory
Mcclelland, J., Mcnaughton, B., and O’Reilly, R · 1995
Earlier work this paper cites.
The rise and fall of thinking machines, 1995
Taubes, G · 1995
Earlier work this paper cites.
Course: 15-880(a) – introduction to neural networks, 1995
Touretzky, D. and Waibel, A · 1995
Earlier work this paper cites.
Synaptic modifications in cultured hippocampal neurons: Dependence on spike timing, synaptic strength, and postsynaptic cell type
Bi, G.-q. and Poo, M.-m · 1998
Earlier work this paper cites.
Hipnet-1: A highly pipelined architecture for neural network training, 03 1998
Kingsbury, B., Morgan, N., and Wawrzynek, J · 1998
Earlier work this paper cites.
When will computer hardware match the human brain
Moravec, H · 1998
Earlier work this paper cites.
Guns, Germs, and Steel: The Fates of Human Societies
Diamond, J., Diamond, P., and Collection, B. H · 1999
Earlier work this paper cites.
Telecosm: How Infinite Bandwidth Will Revolutionize Our World
Gilder, G · 2000
Earlier work this paper cites.
Signal-processing machines at the postsynaptic density
Kennedy, M. B · 2000
Earlier work this paper cites.
The Computer and the Brain
Von Neumann, J., Churchland, P., and Churchland, P · 2000
Earlier work this paper cites.
Automated empirical optimizations of software and the atlas project
Clint Whaley, R., Petitet, A., and Dongarra, J. J · 2001
Earlier work this paper cites.
The anna karenina principle applied to ecological risk assessments of multiple stressors
Moore, D · 2001
Earlier work this paper cites.
When and where do we apply what we learn? a taxonomy for far transfer
Barnett, S. M. and Ceci, S · 2002
Earlier work this paper cites.
Torch: A modular machine learning software library, 11 2002
Collobert, R., Bengio, S., and Marithoz, J · 2002
Earlier work this paper cites.
Technical report: Lush reference manual, code available at http://lush.sourceforge.net, 2002
Lecun, Y. and Bottou, L · 2002
Earlier work this paper cites.
Episodic memory: From mind to brain
Tulving, E · 2002
Earlier work this paper cites.
Understanding the efficiency of gpu algorithms for matrix-matrix multiplication
Fatahalian, K., Sugerman, J., and Hanrahan, P · 2004
Earlier work this paper cites.
Gpu implementation of neural networks
Oh, K.-S. and Jung, K · 2004
Earlier work this paper cites.
The Handbook of Multisensory Processes
Stein, G., Calvert, G., Spence, C., Spence, D., Stein, B., and Stein, P · 2004
Earlier work this paper cites.
When and how to develop domain-specific languages
Mernik, M., Heering, J., and Sloane, A. M · 2005
Earlier work this paper cites.
Accelerated 2d image processing on gpus
Payne, B. R., Belkasim, S. O., Owen, G. S., Weeks, M. C., and Zhu, Y · 2005
Earlier work this paper cites.
The landscape of parallel computing research: A view from berkeley
Asanović, K., Bodik, R., Catanzaro, B. C., Gebis, J. J., Husbands, P., Keutzer, K., Patterson, D. A., Plishker, W. L., Shalf, J., Williams, S. W., and Yelick, K. A · 2006
Earlier work this paper cites.
High performance convolutional neural networks for document processing, 10 2006
Chellapilla, K., Puri, S., and Simard, P · 2006
Cited alongside, same era.
Reconfigurable Computing: The Theory and Practice of FPGA-Based Computation
Hauck, S. and DeHon, A · 2007
Cited alongside, same era.
Core knowledge
Spelke, E. S. and Kinzler, K. D · 2007
Cited alongside, same era.
A synthetic biology challenge: Making cells compute
Tan, C., Song, H., Niemi, J., and You, L · 2007
Cited alongside, same era.
The Computational Limits of Deep Learning
Thompson, N. C., Greenewald, K., Lee, K., and Manso, G. F · 2007
Cited alongside, same era.
Accelerating ai: Past, present, and future, 2018
Asanovic, K · 2018
Later among the works it cites.
Relational inductive biases, deep learning, and graph networks
Battaglia, P. W., Hamrick, J. B., Bapst, V., Sanchez-Gonzalez, A., Zambaldi, V. F., Malinowski, M., Tacchetti, A., Raposo, D., Santoro, A., Faulkner, R., Gülçehre, Ç., Song, H. F., Ballard, A. J., Gilmer, J., Dahl, G. E., Vaswani, A., Allen, K. R., Nash, C., Langston, V., Dyer, C., Heess, N., Wierstra, D., Kohli, P., Botvinick, M., Vinyals, O., Li, Y., and Pascanu, R · 2018
Later among the works it cites.
Darpa announces next phase of electronics resurgence initiative, 2018
DARPA · 2018
Later among the works it cites.
Autotuning numerical dense linear algebra for batched computation with gpu hardware accelerators
Dongarra, J., Gates, M., Kurzak, J., Luszczek, P., and Tsai, Y. M · 2018
Later among the works it cites.
Do better imagenet models transfer better?
Kornblith, S., Shlens, J., and Le, Q. V · 2018
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Thompson, N. C., Greenewald, K., Lee, K., and Manso, G. F · 2007
Cited alongside, same era.
Memory and the computational brain: Why cognitive science will transform neuroscience, 04 2009
Gallistel, C. and King, A · 2009
Cited alongside, same era.
Spending moore’s dividend
Larus, J · 2009
Cited alongside, same era.
Prediction, cognition and the brain
Bubic, A., Von Cramon, D. Y., and Schubotz, R · 2010
Cited alongside, same era.
Deep Big Simple Neural Nets Excel on Handwritten Digit Recognition
Claudiu Ciresan, D., Meier, U., Gambardella, L. M., and Schmidhuber, J · 2010
Cited alongside, same era.
Artificial neural networks in hardware: A survey of two decades of progress
Misra, J. and Saha, I · 2010
Cited alongside, same era.
Software bloat analysis: Finding, removing, and preventing performance problems in modern large-scale object-oriented applications, 01 2010
Xu, H., Mitchell, N., Arnold, M., Rountev, A., and Sevitsky, G · 2010
Cited alongside, same era.
Later among the works it cites.
China plans 47 47 billion fund to boost its semiconductor industry, 2018
Kubota, Y · 2018
Later among the works it cites.
The next step in facebook ai hardware infrastructure, 2018
Lee, K. and Wang, X · 2018
Later among the works it cites.
All-optical machine learning using diffractive deep neural networks
Lin, X., Rivenson, Y., Yardimci, N. T., Veli, M., Luo, Y., Jarrahi, M., and Ozcan, A · 2018
Later among the works it cites.
Big bets on a.i. open a new frontier for chip start-ups, too, 2018
Metz, C · 2018
Later among the works it cites.
Continual Lifelong Learning with Neural Networks: A Review
Parisi, G. I., Kemker, R., Part, J. L., Kanan, C., and Wermter, S · 2018
Later among the works it cites.
Computer history 1949 - 1960 early vacuum tube computers overview, 2018
Project, C. H. A · 2018
Later among the works it cites.
What makes tpus fine-tuned for deep learning?, 2018
Sato, K · 2018
Later among the works it cites.
The decline of computers as a general purpose technology: Why deep learning and the end of moore’s law are fragmenting computing, 2018
Thompson, N. and Spanuth, S · 2018
Later among the works it cites.
Haq: Hardware-aware automated quantization
Wang, K., Liu, Z., Lin, Y., Lin, J., and Han, S · 2018
Later among the works it cites.
Machine learning systems are stuck in a rut
Barham, P. and Isard, M · 2019
Later among the works it cites.
Validating quantum computers using randomized model circuits, September 2019
Cross, A. W., Bishop, L. S., Sheldon, S., Nation, P. D., and Gambetta, J. M · 2019
Later among the works it cites.
Progress in neuromorphic computing : Drawing inspiration from nature for gains in ai and computing
Davies, M · 2019
Later among the works it cites.
Rigging the Lottery: Making All Tickets Winners
Evci, U., Gale, T., Menick, J., Castro, P. S., and Elsen, E · 2019
Later among the works it cites.
The era of general purpose computers is ending, 2019
Feldman, M · 2019
Later among the works it cites.
The state of sparsity in deep neural networks, 2019
Gale, T., Elsen, E., and Hooker, S · 2019
Later among the works it cites.
Efficientnet-edgetpu: Creating accelerator-optimized neural networks with automl, 2019
Gupta, S. and Tan, M · 2019
Later among the works it cites.
The end of moore’s law, cpus (as we know them), and the rise of domain specific architectures, 2019
Hennessy, J · 2019
Later among the works it cites.
What Do Compressed Deep Neural Networks Forget?
Hooker, S., Courville, A., Clark, G., Dauphin, Y., and Frome, A · 2019
Later among the works it cites.
Backpropagation through time and the brain
Lillicrap, T. P. and Santoro, A · 2019
Later among the works it cites.
Energy and policy considerations for deep learning in nlp, 2019
Strubell, E., Ganesh, A., and McCallum, A · 2019
Later among the works it cites.
The bitter lesson, 2019
Sutton, R · 2019
Later among the works it cites.
TinyML: Machine Learning with TensorFlow Lite on Arduino and Ultra-Low-Power Microcontrollers
Warden, P. and Situnayake, D · 2019
Later among the works it cites.
Do we still need models or just more data and compute?, 2019
Welling, M · 2019
Later among the works it cites.
A critique of pure learning: What artificial neural networks can learn from animal brains
Zador, A. M · 2019
Later among the works it cites.
Hawq: Hessian aware quantization of neural networks with mixed-precision, 10 2019
Zhen, D., Yao, Z., Gholami, A., Mahoney, M., and Keutzer, K · 2019
Later among the works it cites.
Enhancing ai performance for iot endpoint devices, 2020
ARM · 2020
Closest in time.
Language Models are Few-Shot Learners
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D. M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCand lish, S., Radford, A., Sutskever, I., and Amodei, D · 2020
Closest in time.
Moore’s law, 2020
CHM · 2020
Closest in time.
1.1 the deep learning revolution and its implications for computer architecture and chip design
Dean, J · 2020
Closest in time.
Which gpu for deep learning?, 2020
Dettmers, T · 2020
Closest in time.
Fast sparse convnets
Elsen, E., Dukhan, M., Gale, T., and Simonyan, K · 2020
Closest in time.
Sparse GPU Kernels for Deep Learning
Gale, T., Zaharia, M., Young, C., and Elsen, E · 2020
Closest in time.
Nvidia ampere architecture in-depth., 2020
Krashinsky, R., Giroux, O., Jones, S., Stam, N., and Ramaswamy, S · 2020
Closest in time.
A scalable pipeline for designing reconfigurable organisms
Kriegman, S., Blackiston, D., Levin, M., and Bongard, J · 2020
Closest in time.
There’s plenty of room at the top: What will drive computer performance after moore’s law?
Leiserson, C. E., Thompson, N. C., Emer, J. S., Kuszmaul, B. C., Lampson, B. W., Sanchez, D., and Schardl, T. B · 2020
Closest in time.
Chip Placement with Deep Reinforcement Learning
Mirhoseini, A., Goldie, A., Yazgan, M., Jiang, J., Songhori, E., Wang, S., Lee, Y.-J., Johnson, E., Pathak, O., Bae, S., Nazi, A., Pak, J., Tong, A., Srinivasa, K., Hang, W., Tuncer, E., Babu, A., Le, Q. V., Laudon, J., Ho, R., Carpenter, R., and Dean, J · 2020
Closest in time.
Mlperf inference benchmark
Reddi, V. J., Cheng, C., Kanter, D., Mattson, P., Schmuelling, G., Wu, C., Anderson, B., Breughe, M., Charlebois, M., Chou, W., Chukka, R., Coleman, C., Davis, S., Deng, P., Diamos, G., Duke, J., Fick, D., Gardner, J. S., Hubara, I., Idgunji, S., Jablin, T. B., Jiao, J., John, T. S., Kanwar, P., Lee, D., Liao, J., Lokhmotov, A., Massa, F., Meng, P., Micikevicius, P., Osborne, C., Pekhimenko, G., Rajan, A. T. R., Sequeira, D., Sirasao, A., Sun, F., Tang, H., Thomson, M., Wei, F., Wu, E., Xu, L., Yamada, K., Yu, B., Yuan, G., Zhong, A., Zhang, P., and Zhou, Y · 2020
Closest in time.
The future of computing beyond moore’s law
Shalf, J · 2020
Closest in time.
Computation on Sparse Neural Networks: an Inspiration for Future Hardware
Sun, F., Qin, M., Zhang, T., Liu, L., Chen, Y.-K., and Xie, Y · 2020
Closest in time.
Openai launches an api to commercialize its research, 2020
Wiggers, K · 2020
Closest in time.
Motion integration and postdiction in visual awareness
Eagleman, D. M. and Sejnowski, T. J · 2036
Closest in time.