Cognitive computer

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A cognitive computer is a computer that hardwires an artificial intelligence and machine-learning algorithms into an integrated circuit (printed circuit board) that closely reproduces the behavior of the human brain.[1] It generally adopts a neuromorphic engineering approach. Synonyms are neuromorphic chip and cognitive chip.[2][3]

An example of a cognitive computer implemented using neural networks and deep learning techniques is IBM's Watson machine.[citation needed] A subsequent development by IBM is the 2014 TrueNorth microchip architecture,[4] which is designed to be closer in structure to the human brain than the von Neumann architecture used in conventional computers.[1] In 2017 Intel also announced its own version of a cognitive chip in "Loihi", which it intended to be available to university and research labs in 2018. Intel, Qualcomm, and others are improving neuromorphic processors steadily, Intel with its Pohoiki Beach and Springs systems.[5][6]

IBM TrueNorth chip[edit]

DARPA SyNAPSE board with 16 TrueNorth chips

TrueNorth was a neuromorphic CMOS integrated circuit produced by IBM in 2014.[7] It is a manycore processor network on a chip design, with 4096 cores, each one having 256 programmable simulated neurons for a total of just over a million neurons. In turn, each neuron has 256 programmable "synapses" that convey the signals between them. Hence, the total number of programmable synapses is just over 268 million (228). Its basic transistor count is 5.4 billion.

Details[edit]

Since memory, computation, and communication are handled in each of the 4096 neurosynaptic cores, TrueNorth circumvents the von Neumann-architecture bottleneck and is very energy-efficient, with IBM claiming a power consumption of 70 milliwatts and a power density that is 1/10,000th of conventional microprocessors.[8] The SyNAPSE chip operates at lower temperatures and power because it only draws power necessary for computation.[9] Skyrmions have been proposed as models of the synapse on a chip.[10][11]

The neurons are emulated using a Linear-Leak Integrate-and-Fire (LLIF) model, a simplification of the leaky integrate-and-fire model.[citation needed]

According to IBM, it doesn't have a clock[12] and operates on unary numbers and computes by counting up to a maximum of 19 bits.[4][13] The said cores are event-driven by using both (a)synchronous logic and are interconnected through an asynchronous packet-switched mesh network on chip (NOC).[13]

IBM has developed a whole new ecosystem to program and use TrueNorth. It included simulator, a new programming language, an integrated programming environment and even libraries.[12] This lack of backward compatibility with any previous technology (e.g. C++ compilers) poses serious vendor lock-in risks and other adverse consequences that may prevent it from commercialization in the future.[12]

Research[edit]

In 2018 a cluster of TrueNorth network-linked to a master computer were used in stereo vision research that attempted to extract the depth of rapidly moving objects in a scene.[14]

Intel Loihi chip[edit]

Intel's self-learning neuromorphic chip, named Loihi (produced in 2017), perhaps named after the Hawaiian seamount Lōʻihi, offers substantial power efficiency designed after the human brain. Intel claims Loihi is about 1000 times more energy efficient than the general-purpose computing power needed to train the neural networks that rival Loihi's performance. In theory, this would support both machine learning training and inference on the same silicon independently of a cloud connection, and more efficient than using convolutional neural networks (CNNs) or deep learning neural networks. Intel points to a system for monitoring a person's heartbeat, taking readings after events such as exercise or eating, and using the cognitive computing chip to normalize the data and work out the ‘normal’ heartbeat. It can then spot abnormalities, but also deal with any new events or conditions.

The first iteration of the Loihi chip was made using Intel's 14 nm fabrication process, and houses 128 clusters of 1,024 artificial neurons each for a total of 131,072 simulated neurons.[15] This offers around 130 million synapses, which is still a rather long way from the human brain's 800 trillion synapses, and behind IBM's TrueNorth, which has around 256 million by using 64 by 4,096 cores.[16] Loihi is now available for research purposes among more than 40 academic research groups as a USB form factor.[17][18] Recent developments include a 64 core chip named Pohoiki Beach (after Isaac Hale Beach Park, also known as Pohoiki).[19]

In October 2019, researchers from Rutgers University published a research paper to demonstrate the energy efficiency of Intel's Loihi in solving Simultaneous localization and mapping.[20]

In March 2020, Intel and Cornell University published a research paper to demonstrate the ability of Intel's Loihi to recognize different hazardous materials, which could eventually aid to "diagnose diseases, detect weapons and explosives, find narcotics, and spot signs of smoke and carbon monoxide".[21]

In September 2021, Intel released Lohi 2, which it claims is roughly same, but faster.[22]

SpiNNaker[edit]

SpiNNaker (Spiking Neural Network Architecture) is a massively parallel, manycore supercomputer architecture designed by the Advanced Processor Technologies Research Group at the Department of Computer Science, University of Manchester.[23]

Criticism[edit]

Critics argue that a room-sized computer – like the case of Watson – is not a viable alternative to a three-pound human brain.[24] Some also cite the difficulty for a single system to bring so many elements together such as the disparate sources of information as well as computing resources.[25]

See also[edit]

References[edit]

  1. ^ a b Witchalls, Clint (November 2014). "A computer that thinks". New Scientist. 224 (2994): 28–29. Bibcode:2014NewSc.224...28W. doi:10.1016/S0262-4079(14)62145-X.
  2. ^ Seo, Jae-sun; Brezzo, Bernard; Liu, Yong; Parker, Benjamin D.; Esser, Steven K.; Montoye, Robert K.; Rajendran, Bipin; Tierno, José A.; Chang, Leland; Modha, Dharmendra S.; Friedman, Daniel J. (September 2011). "A 45nm CMOS neuromorphic chip with a scalable architecture for learning in networks of spiking neurons". 2011 IEEE Custom Integrated Circuits Conference (CICC): 1–4. doi:10.1109/CICC.2011.6055293. ISBN 978-1-4577-0222-8. S2CID 18690998. Retrieved 21 December 2021.
  3. ^ "Samsung plugs IBM's brain-imitating chip into an advanced sensor". Engadget. Retrieved 21 December 2021.
  4. ^ a b "The brain's architecture, efficiency… on a chip". IBM Research Blog. 2016-12-19. Retrieved 2021-08-21.
  5. ^ Intel’s Pohoiki Beach, a 64-Chip Neuromorphic System, Delivers Breakthrough Results in Research Tests
  6. ^ "Korean Researchers Develop World's First Skyrmion-based Artificial Synapse Component". 30 March 2020.
  7. ^ Merolla, P. A.; Arthur, J. V.; Alvarez-Icaza, R.; Cassidy, A. S.; Sawada, J.; Akopyan, F.; Jackson, B. L.; Imam, N.; Guo, C.; Nakamura, Y.; Brezzo, B.; Vo, I.; Esser, S. K.; Appuswamy, R.; Taba, B.; Amir, A.; Flickner, M. D.; Risk, W. P.; Manohar, R.; Modha, D. S. (2014). "A million spiking-neuron integrated circuit with a scalable communication network and interface". Science. 345 (6197): 668–73. Bibcode:2014Sci...345..668M. doi:10.1126/science.1254642. PMID 25104385. S2CID 12706847.
  8. ^ https://spectrum.ieee.org/computing/hardware/how-ibm-got-brainlike-efficiency-from-the-truenorth-chip How IBM Got Brainlike Efficiency From the TrueNorth Chip
  9. ^ "Cognitive computing: Neurosynaptic chips". IBM. 11 December 2015.
  10. ^ Song, Kyung Mee; Jeong, Jae-Seung; Pan, Biao; Zhang, Xichao; Xia, Jing; Cha, Sunkyung; Park, Tae-Eon; Kim, Kwangsu; Finizio, Simone; Raabe, Jörg; Chang, Joonyeon; Zhou, Yan; Zhao, Weisheng; Kang, Wang; Ju, Hyunsu; Woo, Seonghoon (March 2020). "Skyrmion-based artificial synapses for neuromorphic computing". Nature Electronics. 3 (3): 148–155. arXiv:1907.00957. doi:10.1038/s41928-020-0385-0. S2CID 195767210.
  11. ^ "Neuromorphic computing: The long path from roots to real life". 15 December 2020.
  12. ^ a b c "IBM Research: Brain-inspired Chip". www.research.ibm.com. 9 February 2021. Retrieved 2021-08-21.
  13. ^ a b Andreou, Andreas G.; Dykman, Andrew A.; Fischl, Kate D.; Garreau, Guillaume; Mendat, Daniel R.; Orchard, Garrick; Cassidy, Andrew S.; Merolla, Paul; Arthur, John; Alvarez-Icaza, Rodrigo; Jackson, Bryan L. (May 2016). "Real-time sensory information processing using the TrueNorth Neurosynaptic System". 2016 IEEE International Symposium on Circuits and Systems (ISCAS): 2911. doi:10.1109/ISCAS.2016.7539214. ISBN 978-1-4799-5341-7. S2CID 29335047.
  14. ^ "Stereo Vision Using Computing Architecture Inspired by the Brain". IBM Research Blog. 2018-06-19. Retrieved 2021-08-21.
  15. ^ "Why Intel built a neuromorphic chip". September 29, 2017. www.ZDNet.com
  16. ^ "Intel unveils Loihi neuromorphic chip, chases IBM in artificial brains". October 17, 2017. AITrends.com
  17. ^ "Intel Ramps up Neuromorphic Computing Effort with New Research Partners | TOP500 Supercomputer Sites".
  18. ^ http://niceworkshop.org/wp-content/uploads/2018/05/Mike-Davies-NICE-Loihi-Intro-Talk-2018.pdf[bare URL PDF]
  19. ^ "Intel's Neuromorphic Loihi Processor Scales to 8M Neurons, 64 Cores - ExtremeTech".
  20. ^ Tang, Guangzhi; Shah, Arpit; Michmizos, Konstantinos. (2019). "Spiking neural network on neuromorphic hardware for energy-efficient unidimensional SLAM". 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS): 4176–4181. arXiv:1903.02504. doi:10.1109/IROS40897.2019.8967864. ISBN 978-1-7281-4004-9. S2CID 70349899.
  21. ^ Imam, Nabil; Cleland, Thomas A. (2020). "Rapid online learning and robust recall in a neuromorphic olfactory circuit". Nature Machine Intelligence. 2 (3): 181–191. arXiv:1906.07067. doi:10.1038/s42256-020-0159-4. S2CID 189928531.
  22. ^ "Neuromorphic Computing - Next Generation of AI". Intel.
  23. ^ "Research Groups: APT - Advanced Processor Technologies (School of Computer Science - the University of Manchester)".
  24. ^ Neumeier, Marty (2012). Metaskills: Five Talents for the Robotic Age. Indianapolis, IN: New Riders. ISBN 9780133359329.
  25. ^ Hurwitz, Judith; Kaufman, Marcia; Bowles, Adrian (2015). Cognitive Computing and Big Data Analytics. Indianapolis, IN: John Wiley & Sons. p. 110. ISBN 9781118896624.

Further reading[edit]