In computing and computer science, a processor or processing unit is an electrical component (digital circuit) that performs operations on an external data source, usually memory or some other data stream.[1] It typically takes the form of a microprocessor, which can be implemented on a single or a few tightly integrated metal–oxide–semiconductor integrated circuit chips.[2][3] In the past, processors were constructed using multiple individual vacuum tubes,[4][5] multiple individual transistors,[6] or multiple integrated circuits.

The term is frequently used to refer to the central processing unit (CPU), the main processor in a system.[7] However, it can also refer to other coprocessors, such as a graphics processing unit (GPU).[8]

Traditional processors are typically based on silicon; however, researchers have developed experimental processors based on alternative materials such as carbon nanotubes,[9] graphene,[10] diamond,[11] and alloys made of elements from groups three and five of the periodic table.[12] Transistors made of a single sheet of silicon atoms one atom tall and other 2D materials have been researched for use in processors.[13] Quantum processors have been created; they use quantum superposition to represent bits (called qubits) instead of only an on or off state.[14][15]

Moore's law

Transistor count over time, demonstrating Moore's law

Moore's law, named after Gordon Moore, is the observation and projection via historical trend that the number of transistors in integrated circuits, and therefore processors by extension, doubles every two years.[16] The progress of processors has followed Moore's law closely.[17]


Central processing units (CPUs) are the primary processors in most computers. They are designed to handle a wide variety of general computing tasks rather than only a few domain-specific tasks. If based on the von Neumann architecture, they contain at least a control unit (CU), an arithmetic logic unit (ALU), and processor registers. In practice, CPUs in personal computers are usually also connected, through the motherboard, to a main memory bank, hard drive or other permanent storage, and peripherals, such as a keyboard and mouse.

Graphics processing units (GPUs) are present in many computers and designed to efficiently perform computer graphics operations, including linear algebra. They are highly parallel, and CPUs usually perform better on tasks requiring serial processing. Although GPUs were originally intended for use in graphics, over time their application domains have expanded, and they have become an important piece of hardware for machine learning.[18]

There are several forms of processors specialized for machine learning. These fall under the category of AI accelerators (also known as neural processing units, or NPUs) and include vision processing units (VPUs) and Google's Tensor Processing Unit (TPU).

Sound chips and sound cards are used for generating and processing audio. Digital signal processors (DSPs) are designed for processing digital signals. Image signal processors are DSPs specialized for processing images in particular.

Deep learning processors, such as neural processing units are designed for efficient deep learning computation.

Physics processing units (PPUs) are built to efficiently make physics-related calculations, particularly in video games.[19]

Field-programmable gate arrays (FPGAs) are specialized circuits that can be reconfigured for different purposes, rather than being locked into a particular application domain during manufacturing.

The Synergistic Processing Element or Unit (SPE or SPU) is a component in the Cell microprocessor.

Processors based on different circuit technology have been developed. One example is quantum processors, which use quantum physics to enable algorithms that are impossible on classical computers (those using traditional circuitry). Another example is photonic processors, which use light to make computations instead of semiconducting electronics.[20] Processing is done by photodetectors sensing light produced by lasers inside the processor.[21]

See also


  1. ^ "Oxford English Dictionary". Lexico. Archived from the original on March 25, 2020. Retrieved 25 March 2020.
  2. ^ "Reading: The Central Processing Unit | Introduction to Computer Applications and Concepts". Retrieved 2022-01-28.
  3. ^ "The Silicon Engine".
  4. ^ Garner, Robert; Dill, Frederick (Rick) (Winter 2010). "The Legendary IBM 1401 Data Processing System" (PDF). IEEE Solid-State Circuits Magazine. 2 (1): 28–39. doi:10.1109/MSSC.2009.935295. S2CID 31608817.
  5. ^ "IBM100 - The IBM 700 Series". 2012-03-07. Retrieved 2022-01-28.
  6. ^ "Megaprocessor". Retrieved 2022-01-28.
  7. ^ "Oxford English Dictionary". Lexico. Archived from the original on March 25, 2020. Retrieved 25 March 2020.
  8. ^ Sakdhnagool, Putt (4 September 2018). "Comparative analysis of coprocessors". Concurrency and Computation Practice and Experience. 31 (1). doi:10.1002/cpe.4756. S2CID 54473111 – via Wiley Online Library.
  9. ^ Hills, Gage; Lau, Christian; Wright, Andrew; Fuller, Samuel; Bishop, Mindy D.; Srimani, Tathagata; Kanhaiya, Pritpal; Ho, Rebecca; Amer, Aya; Stein, Yosi; Murphy, Denis (2019-08-29). "Modern microprocessor built from complementary carbon nanotube transistors". Nature. 572 (7771): 595–602. Bibcode:2019Natur.572..595H. doi:10.1038/s41586-019-1493-8. ISSN 0028-0836. PMID 31462796. S2CID 201658375.
  10. ^ Akinwande, Deji; Huyghebaert, Cedric; Wang, Ching-Hua; Serna, Martha I.; Goossens, Stijn; Li, Lain-Jong; Wong, H.-S. Philip; Koppens, Frank H. L. (2019-09-26). "Graphene and two-dimensional materials for silicon technology". Nature. 573 (7775): 507–518. Bibcode:2019Natur.573..507A. doi:10.1038/s41586-019-1573-9. ISSN 0028-0836. PMID 31554977. S2CID 202762945.
  11. ^ "Using artificial intelligence to engineer materials' properties". 11 February 2019.
  12. ^ Riel, Heike; Wernersson, Lars-Erik; Hong, Minghwei; del Alamo, Jesús A. (August 2014). "III–V compound semiconductor transistors—from planar to nanowire structures". MRS Bulletin. 39 (8): 668–677. doi:10.1557/mrs.2014.137. hdl:1721.1/99977. ISSN 0883-7694. S2CID 138353703.
  13. ^ Li, Ming-Yang; Su, Sheng-Kai; Wong, H.-S. Philip; Li, Lain-Jong (March 2019). "How 2D semiconductors could extend Moore's law". Nature. 567 (7747): 169–170. Bibcode:2019Natur.567..169L. doi:10.1038/d41586-019-00793-8. ISSN 0028-0836. PMID 30862924. S2CID 75136648.
  14. ^ "quantum computer | Description & Facts | Britannica". Retrieved 2022-01-28.
  15. ^ "Experimental Implementation of Fast Quantum Searching" (PDF).
  16. ^ "Moore's law: computer science". Retrieved 2022-01-28.
  17. ^ "Moore's Law". Retrieved 2022-01-28.
  18. ^ "CPU vs. GPU: What's the Difference?". Intel. Retrieved 2022-02-27.
  19. ^ "Revolution in Gaming: Physics Processing Units (PPUs) Elevate Realism with Efficient Physics-Related Calculations". PCMasters (in German). Retrieved 2023-08-10.
  20. ^ Sun, Chen; Wade, Mark T.; Lee, Yunsup; Orcutt, Jason S.; Alloatti, Luca; Georgas, Michael S.; Waterman, Andrew S.; Shainline, Jeffrey M.; Avizienis, Rimas R.; Lin, Sen; Moss, Benjamin R. (December 2015). "Single-chip microprocessor that communicates directly using light". Nature. 528 (7583): 534–538. Bibcode:2015Natur.528..534S. doi:10.1038/nature16454. ISSN 0028-0836. PMID 26701054. S2CID 205247044.
  21. ^ Yang, Sarah (2015-12-23). "Engineers demo first processor that uses light for ultrafast communications". Berkeley News. Retrieved 2022-01-28.