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Separate from the stack definition of a MISC architecture, is the MISC architecture being defined by the number of instructions supported. Typically a minimal instruction set computer is viewed as having 32 or fewer instructions, [1] [2] [3] where NOP, RESET, and CPUID type instructions are usually not counted by consensus due to their fundamental nature.
Computer-assisted language learning (CALL), known as computer-aided instruction (CAI) in British English and computer-aided language instruction (CALI) in American English, [1] Levy (1997: p.
PDP-11 CPU board. Computer hardware includes the physical parts of a computer, such as the central processing unit (CPU), random access memory (RAM), motherboard, computer data storage, graphics card, sound card, and computer case.
Natural language processing (NLP) is a subfield of computer science and especially artificial intelligence.It is primarily concerned with providing computers with the ability to process data encoded in natural language and is thus closely related to information retrieval, knowledge representation and computational linguistics, a subfield of linguistics.
The IBM 1401 is a variable-wordlength decimal computer that was announced by IBM on October 5, 1959. The first member of the highly successful IBM 1400 series, it was aimed at replacing unit record equipment for processing data stored on punched cards and at providing peripheral services for larger computers. [1]
The integrated circuit is an essential invention to produce modern software systems. [2]The first use of the word software is credited to mathematician John Wilder Tukey in 1958. [3]
There are many differences between high-throughput computing, high-performance computing (HPC), and many-task computing (MTC). HPC tasks are characterized as needing large amounts of computing power for short periods of time, whereas HTC tasks also require large amounts of computing, but for much longer times (months and years, rather than hours and days).
Bidirectional encoder representations from transformers (BERT) is a language model introduced in October 2018 by researchers at Google. [1] [2] It learns to represent text as a sequence of vectors using self-supervised learning.