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Advancements in Microprocessor Architecture for Ubiquitous AI—An Overview on History, Evolution, and Upcoming Challenges in AI Implementation

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Advancements in Microprocessor Architecture for Ubiquitous AI—An Overview on History, Evolution, and Upcoming Challenges in AI Implementation micromachines Review Advancements in Microprocessor Architecture for Ubiquitous AI—An Overview on History, Evolution, and Upcoming Challenges in AI Implementation Fatima Hameed Khan, Muhammad Adeel Pasha * and Shahid Masud * Department of Electrical Engineering, Lahore University of Management Sciences (LUMS), Lahore, Punjab 54792, Pakistan; [email protected] * Correspondence: [email protected] (M.A.P.); [email protected] (S.M.) Abstract: Artificial intelligence (AI) has successfully made its way into contemporary industrial sectors such as automobiles, defense, industrial automation 4.0, healthcare technologies, agriculture, and many other domains because of its ability to act autonomously without continuous human interventions. However, this capability requires processing huge amounts of learning data to extract useful information in real time. The buzz around AI is not new, as this term has been widely known for the past half century. In the 1960s, scientists began to think about machines acting more like humans, which resulted in the development of the first natural language processing computers. It laid the foundation of AI, but there were only a handful of applications until the 1990s due to limitations in processing speed, memory, and computational power available. Since the 1990s, advancements in computer architecture and memory organization have enabled microprocessors to deliver much higher performance. Simultaneously, improvements in the understanding and mathematical representation of AI gave birth to its subset, referred to as machine learning (ML). ML Citation: Khan, F.H.; Pasha, M.A.; includes different algorithms for independent learning, and the most promising ones are based on Masud, S. Advancements in brain-inspired techniques classified as artificial neural networks (ANNs). ANNs have subsequently Microprocessor Architecture for evolved to have deeper and larger structures and are often characterized as deep neural networks Ubiquitous AI—An Overview on History, Evolution, and Upcoming (DNN) and convolution neural networks (CNN). In tandem with the emergence of multicore pro- Challenges in AI Implementation. cessors, ML techniques started to be embedded in a range of scenarios and applications. Recently, Micromachines 2021, 12, 665. application-specific instruction-set architecture for AI applications has also been supported in differ- https://doi.org/10.3390/mi12060665 ent microprocessors. Thus, continuous improvement in microprocessor capabilities has reached a stage where it is now possible to implement complex real-time intelligent applications like computer Academic Editor: Piero Malcovati vision, object identification, speech recognition, data security, spectrum sensing, etc. This paper presents an overview on the evolution of AI and how the increasing capabilities of microprocessors Received: 5 May 2021 have fueled the adoption of AI in a plethora of application domains. The paper also discusses the Accepted: 3 June 2021 upcoming trends in microprocessor architectures and how they will further propel the assimilation Published: 6 June 2021 of AI in our daily lives. Publisher’s Note: MDPI stays neutral Keywords: artificial intelligence; microprocessors; instruction set architecture; application-specific in- with regard to jurisdictional claims in tegrated circuits; real-time processing; machine learning; intelligent systems; automation; multicores published maps and institutional affil- iations. 1. Introduction Artificial intelligence (AI) is a thriving tool that has coupled human intelligence and Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. machine efficiency to excel in various disciplines of life. The idea of building intelligent This article is an open access article machines is even older than the field of AI itself. In 1950, Alan Turing presented the distributed under the terms and possibility of implementing an intelligent machine and gave the parameters to judge conditions of the Creative Commons its intelligence, known as the Turing test [1]. The term “artificial intelligence” was first Attribution (CC BY) license (https:// mentioned in the Dartmouth Conference in 1956, which was attended by those who later creativecommons.org/licenses/by/ became the leading figures of this field. Early AI research included several programs 4.0/). and methodologies, such as General Problem Solver [2], Theorem Prover [3], natural Micromachines 2021, 12, 665. https://doi.org/10.3390/mi12060665 https://www.mdpi.com/journal/micromachines Micromachines 2021, 12, x 2 of 23 telligence, known as the Turing test [1]. The term “artificial intelligence” was first men- tioned in the Dartmouth Conference in 1956, which was attended by those who later be- Micromachines 2021, 12, 665 came the leading figures of this field. Early AI research included several programs2 of 22 and methodologies, such as General Problem Solver [2], Theorem Prover [3], natural language processor ELIZA [4] and the discovery of the perceptron [5]. Intrigued by the success of theselanguage projects, processor the Defense ELIZA [Advanced4] and the discoveryResearch ofProjects the perceptron Agency [(DARPA)5]. Intrigued also by invested the massivelysuccess of thesein this projects, field to theutilize Defense machine Advanced intelligence Research for Projectsvarious Agencynational (DARPA) security projects also [6].invested After massivelythis first wave, in this AI field encountered to utilize machine technological intelligence barriers for various that curtailed national the security vast ex- pectationsprojects [6]. of After AI. thisIn the first following wave, AI decades, encountered as the technological market for barriers personal that computers curtailed the (PCs) wasvast established, expectations the of AI.paradigm In the following of AI shifted decades, to a knowledge-based-system, as the market for personal computersalso known as Expert(PCs) was Systems established, [7]. It was the programmed paradigm of AIusing shifted symbolic to a knowledge-based-system, programming languages alsosuch as LISPknown or asProlog. Expert Expert Systems Systems [7]. It was were programmed used to implement using symbolic human programming expert knowledge languages in a machinesuch as LISP that or makes Prolog. decisions Expert Systems based on were stored used information to implement [8]. human Due expertto problems knowledge in accu- in a machine that makes decisions based on stored information [8]. Due to problems racy and efficiency, Expert Systems could not find widespread acceptability in the market. in accuracy and efficiency, Expert Systems could not find widespread acceptability in Finally, the increase in computing power and development of more sophisticated the market. mathematicalFinally, the modeling increase tools in computing gave birth power to a new and AI development paradigm that of morebecame sophisticated more success- fulmathematical than before. modeling The more tools advanced gave birth subset to a new of AI AI algorithms paradigm that in the became form more of machine successful learn- ingthan (ML) before. addressed The more the advanced complex subset problems of AI of algorithms AI and showed in the form a promising of machine way learning forward because(ML) addressed of its ability the complex to make problems autonomous of AI and decisions showed based a promising on previous way forward learning because and the scenariosof its ability at tohand make [9]. autonomous The algorithms decisions used based in ML on previouscan find learning applications and the in scenarios diverse use cases,at hand such [9]. as The the algorithms use of decision used trees in ML to can mo findnitor applications the depth of in anesthesia diverse use [10] cases, and suchsupport vectoras the machines use of decision (SVM) trees in financial to monitor research the depth [11]. of Artificial anesthesia neural [10] andnetworks support (ANN) vector have notmachines only outperformed (SVM) in financial other research ML algorithms [11]. Artificial but also neural surpassed networks human (ANN) intelligence have not in specificonly outperformed tasks, e.g., image other ML classification algorithms on but an also ImageNet surpassed dataset human [12]. intelligence It motivated in specific research- erstasks, to delve e.g., image deeper classification into the ANN on structure, an ImageNet which dataset resulted [12]. in It networks motivated with researchers more param- to eters,delve layers, deeper and into operations, the ANN structure, which came which to resulted be classified in networks as deep with neural more networks parameters, (DNN) [13].layers, DNNs and operations,can be further which divided came tointo be structured classified as network deep neural techniques networks for (DNN) various [13 appli-]. DNNs can be further divided into structured network techniques for various applications, cations, i.e., recurrent neural networks (RNN) and transformer networks that mainly fo- i.e., recurrent neural networks (RNN) and transformer networks that mainly focus on cus on natural language processing. In recent years, AI has revolutionized society by cov- natural language processing. In recent years, AI has revolutionized society by covering a eringwide a range wide of range applications, of applications, from the from simplest the simplest smartphones smartphones in our hands in our to hands security to security and andsurveillance surveillance [14] and[14] autonomousand autonomous vehicle vehicle controls co [ntrols15]. Currently, [15].
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