Cognitive computer

Source: Wikipedia, the free encyclopedia.

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

In 2023, IBM's proof-of-concept NorthPole chip achieved remarkable performance in

image recognition.[4]

In 2013, IBM developed

TrueNorth microchip architecture,[6] 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 version of a cognitive chip in "Loihi", which it intended to be available to university and research labs in 2018. Intel (most notably with its Pohoiki Beach and Springs systems[7][8]), Qualcomm
, and others are improving neuromorphic processors steadily.

IBM TrueNorth chip

DARPA SyNAPSE board with 16 TrueNorth chips

TrueNorth was a

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

Memory, computation, and communication are handled in each of the 4096 neurosynaptic cores, TrueNorth circumvents the

milliwatts and a power density that is 1/10,000th of conventional microprocessors.[10] The SyNAPSE chip operates at lower temperatures and power because it only draws power necessary for computation.[11] Skyrmions have been proposed as models of the synapse on a chip.[12][13]

The neurons are emulated using a Linear-Leak Integrate-and-Fire (LLIF) model, a simplification of the leaky

integrate-and-fire model.[14]

According to IBM, it does not have a clock,[15] operates on unary numbers, and computes by counting to a maximum of 19 bits.[6][16] The cores are event-driven by using both synchronous and asynchronous logic, and are interconnected through an asynchronous packet-switched mesh network on chip (NOC).[16]

IBM developed a new network to program and use TrueNorth. It included a simulator, a new programming language, an integrated programming environment, and libraries.[15] 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.[15][failed verification]

Research

In 2018, a cluster of TrueNorth network-linked to a master computer was used in stereo vision research that attempted to extract the depth of rapidly moving objects in a scene.[17]

IBM NorthPole chip

In 2023, IBM released its NorthPole chip, which is a

processor cores and can perform 2,048 operations per core per cycle at 8-bit precision, and 8,192 operations at 2-bit precision. It runs at between 25 and 425 MHz. [4][18][19][20]
This is an inferencing chip, but it cannot yet handle GPT-4.

Intel Loihi chip

Pohoiki Springs

Pohoiki Springs is a system that incorporates Intel's self-learning neuromorphic chip, named Loihi, introduceed in 2017, perhaps named after the Hawaiian

Lōʻihi. Intel claims Loihi is about 1000 times more energy efficient than general-purpose computing systems used to train neural networks. In theory, Loihi supports both machine learning training and inference on the same silicon independently of a cloud connection, and more efficiently than convolutional neural networks 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 chip to normalize the data and work out the ‘normal’ heartbeat. It can then spot abnormalities and deal with new events or conditions.

The first iteration of the chip was made using Intel's 14 nm fabrication process and houses 128 clusters of 1,024

TrueNorth.[22] Loihi is available for research purposes among more than 40 academic research groups as a USB form factor.[23][24]

In October 2019, researchers from

energy efficiency of Intel's Loihi in solving simultaneous localization and mapping.[25]

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".[26]

Hala Point

Intel's Loihi 2, named Pohoiki, was released in September 2021 with 64 cores.[27] It boasts faster speeds, higher-bandwidth inter-chip communications for enhanced scalability, increased capacity per chip, a more compact size due to process scaling, and improved programmability.[28]

Intel claimed in 2024 that Hala Point was the world’s largest neuromorphic system. It uses Loihi 2 chips. It is claimed to offer 10x more neuron capacity and up to 12x higher performance.

Hala Point provides up to 20 quadrillion operations per second, (20 petaops), with efficiency exceeding 15 trillion (8-bit) operations S-1 W-1 on conventional deep neural networks. This rivals levels achieved by GPU/CPU architectures. Hala Point packages 1,152 Loihi 2 processors produced on Intel 3 process node in a six-rack-unit chassis. The system supports up to 1.15 billion neurons and 128 billion synapses distributed over 140,544 neuromorphic processing cores, consuming 2,600 watts of power. It includes over 2,300 embedded x86 processors for ancillary computations.

Hala Point integrates processing, memory and communication channels in a massively parallelized fabric, providing 16 PB S-1 of memory bandwidth, 3.5 PB S-1 of inter-core communication bandwidth, and 5 TB S-1 of inter-chip bandwidth.

The system can process its 1.15 billion neurons 20 times faster than a human brain. Its neuron capacity is roughly equivalent to that of an owl brain or the cortex of a capuchin monkey.

Loihi-based systems can perform inference and optimization using 100 times less energy at speeds as much as 50 times faster than CPU/GPU architectures.

SpiNNaker

manycore supercomputer architecture designed by the Advanced Processor Technologies Research Group at the Department of Computer Science, University of Manchester.[29]

Criticism

Critics argue that a room-sized computer – as in the case of

Watson – is not a viable alternative to a three-pound human brain.[30] 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.[31]

In 2021, The New York Times released Steve Lohr's article "What Ever Happened to IBM’s Watson?".[32] He wrote about some costly failures of IBM Watson. One of them, a cancer-related project called the Oncology Expert Advisor,[33] was abandoned in 2016 as a costly failure. During the collaboration, Watson could not use patient data. Watson struggled to decipher doctors’ notes and patient histories.

See also

References

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  3. ^ "Samsung plugs IBM's brain-imitating chip into an advanced sensor". Engadget. Retrieved 21 December 2021.
  4. ^ a b "IBM Debuts Brain-Inspired Chip For Speedy, Efficient AI - IEEE Spectrum". spectrum.ieee.org. Retrieved 2023-10-30.
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  6. ^ a b "The brain's architecture, efficiency… on a chip". IBM Research Blog. 2016-12-19. Retrieved 2021-08-21.
  7. ^ "Intel's Pohoiki Beach, a 64-Chip Neuromorphic System, Delivers Breakthrough Results in Research Tests". Intel Newsroom.
  8. ^ "Korean Researchers Devel". 30 March 2020.
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  10. ^ https://spectrum.ieee.org/computing/hardware/how-ibm-got-brainlike-efficiency-from-the-truenorth-chip How IBM Got Brainlike Efficiency From the TrueNorth Chip
  11. ^ "Cognitive computing: Neurosynaptic chips". IBM. 11 December 2015.
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  13. ^ "Neuromorphic computing: The long path from roots to real life". 15 December 2020.
  14. ^ "The brain's architecture, efficiency… on a chip". IBM Research Blog. 2016-12-19. Retrieved 2022-09-28.
  15. ^ a b c "IBM Research: Brain-inspired Chip". www.research.ibm.com. 9 February 2021. Retrieved 2021-08-21.
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  17. ^ "Stereo Vision Using Computing Architecture Inspired by the Brain". IBM Research Blog. 2018-06-19. Retrieved 2021-08-21.
  18. ^ Afifi-Sabet, Keumars (2023-10-28). "Inspired by the human brain — how IBM's latest AI chip could be 25 times more efficient than GPUs by being more integrated — but neither Nvidia nor AMD have to worry just yet". TechRadar. Retrieved 2023-10-30.
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  21. ^ "Why Intel built a neuromorphic chip". ZDNET.
  22. ^ ""Intel unveils Loihi neuromorphic chip, chases IBM in artificial brains". October 17, 2017. AITrends.com". Archived from the original on August 11, 2021. Retrieved October 17, 2017.
  23. ^ Feldman, M. (7 December 2018). "Intel Ramps Up Neuromorphic Computing Effort with New Research Partners". TOP500. Retrieved 22 December 2023.
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  27. ^ Hruska, J. (16 July 2019). "Intel's Neuromorphic Loihi Processor Scales to 8M Neurons, 64 Cores". Ziff Davis. Retrieved 22 December 2023.
  28. ^ Peckham, Oliver (2022-09-28). "Intel Labs Launches Neuromorphic 'Kapoho Point' Board". HPCwire. Retrieved 2023-10-26.
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Further reading
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