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Artificial Intelligence and its Functional Units | AI

Artificial Intelligence | AI | Artificial Intelligence and its Functional Units

Artificial intelligence belongs to one of the advanced technologies determined in the present time. It enables machines to learn, reason, think, act, and solve situations just the same as humans do. Artificial intelligence spread to vast areas of fields like marketing, training, modern sports education, brain-inspired cognitive AI, AI in the medical stream (prediction of post mortem Interval, etc.), Artificial Intelligence of things wearable systems for cardiac detection, and many more.

So, AI is defined as intelligence demonstrated by machines, unlike the natural Intelligence displayed by humans and animals. Generally, if we talk about Artificial Intelligence, it can be classified in two ways- Based on capabilities and based on functionality.

Classification of Artificial Intelligence on the Bases of Capability and Functionality

Capability BasesFunctionality Bases
Narrow or Weak AI (mimic like humans)  Reactive Machines
General AI (Just like humans)  Limited Memory
Strong or Superior AI (Superior to Humans)  Theory of Mind

As for the implementation, there are some resources on which technology is built. If we talk about Artificial intelligence, the prime resource is the hardware on which AI gets stable after it the next thing is ANN (Artificial Neural Network), and another resource that makes it an advancement for the upcoming future is Deep Learning which is a subset of ANN or machine learning.

Neural Processing Unit (NPU)

The hardware on which AI resides is NPU (Neural Processing Unit) or Neural Processor. It is a neural processing engine which is a specialized circuit that implements all the necessary control and arithmetic logic necessary to execute machine learning algorithms, typically by operating on predictive models such as Artificial Neural Networks (ANNs) or Random Forests (RFs). However, NPU is classified into two categories that are training and inference.

Processors Equipped with Integrated or dedicated NPUs

BrandProcessor nameN.P.U. (Neural Processing Unit)Process technologyLaunching dates
QualcommSnapdragon 865+Dedicated Quadcore Hexagonal tensor NPU accelerator7nm8 July 2020
 Snapdragon 865Dedicated Quadcore Hexagonal tensor NPU accelerator7nmMarch 2020
 Snapdragon 765Hexagon 696
HVX + Tensor 5.4TOPS AI
7nm4 December 2019
MediatekDimesity 800Integrated quad-core NPU7nmMarch 2020
 Dimesity 820Integrated mediated APU 3.0(tri-core N.P.U.)7nm2nd quarter of 2020
 Dimesity 1000Integrated APU 30 hexacore NPU7nm23 April 2019
SamsungExynos 990Dual-core NPU7nm2020
 Exynos 9825Integrated NPU7nm2019
 Exynos 980Integrated NPU8nm finFETEnd of 2019
AppleA13 Bionic ChipOcta-core Neural engine7nm10 Sept 2019
 A12 Bionic chipOcta-core Neural engine7nm FinFET12 Sept 2018
 A11 Bionic chipDedicated neural network10 nm FinFET12 Sept 2017
HuaweiHisilicon kirin 990Dedicated dual-core DaVinci NPU7nm6 Sept 2019
 Hisilicon kirin 980Dedicated dual-core NPU7nm31 August 2018
 Hisilicon kirin 970Dedicated NPU10nm1 Sept 2017

Training NPUs

Training NPUs are used for training or preparation purposes for the new models. It works by putting the values in the algorithms by guessing the correct values. It guesses the different values or data until it gets perfectly fits the convenient, more correct, and smooth working of the algorithms designed for accelerating the training of new models.

Inference NPUs

Inference NPUs are utilized to implement inference operations on completely trained models. In fact, these are used to accelerate the input of a piece of data (a picture or image), after which it gets processed by other trained models in order to get desired results.

Artificial Neural Networks (ANNs)

Now one of the subsets of AI is Artificial Neural Networks (ANNs) sometimes also called machine learning (ML). It is related to a part of NPU and works as application software for it. Basically, ANN is a subset of Artificial intelligence that is programmed in an NPU circuit as an application that has various interconnected nodes to pass the logical inputs and manipulates them according to the situation with the help of mathematical algorithms.

Artificial Intelligence

These algorithms are designed to work just like the neurons working in a brain of a human or animal. In short, ANN is defined as computing systems vaguely inspired by the biological neural networks that constitute animal brains. Now if we go in advance ANN then it is known by the term that is Deep Learning (DL).

Deep Learning (DL)

DL is a subset of machine learning or ANN in artificial intelligence (AI) that has networks capable of learning unsupervised from data that is unstructured and unlabeled. It is sometimes also known as Deep neural learning or deep neural network.

DL is the advanced neural processing of ANN in which random data inputs are arranged in a hierarchical format to predict possible situations just like the natural predictive brain of humans. It uses the hierarchical pattern to detect speech recognition, face detection, finding anomalous and suspicious activities by fraud, etc.

Artificial Intelligence

According to this study, deep learning is a subset or collaboration to the artificial neural network (ANN) or machine learning is a subset of artificial intelligence (AI) and AI resides (collaborated) on NPU as physical existence. All these things are related to each other to mimic and solve real-world situations and problems just like a human brain.

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