{"title":"AI for Consumer Electronics - Has Come a Long Way But Has a Long Way to Go","authors":"S. Mohanty","doi":"10.1109/mce.2020.2968754","DOIUrl":null,"url":null,"abstract":"It reminds me that IEEE MCE has covered intelligent electronics, smart electronics kind articles in some of its past issues. In addition, I guest edited a special issue on smart electronics in IEEE Potentials. In Jan 2019 issue of IEEE Potentials, I defined smart electronics as the class C E systems that are envisioned to be Energy-Smart, Security-Smart, and Response-Smart. I advocated that these 3 key aspects and design trade-offs among them is the crucial for the next generation CE. In fact, in my booked titled “Nanoelectronic Mixed-Signal Systems” published in 2015, I presented a broad perspective for design trade-offs of CE systems under the theme “Design of Excellence (DFX)” or ‘Design of X (DFX)”. In DFX, “X” refers to a subset of characteristics/figures-of-merit (FoMs), such as energy, speed, security, and safety, making it Design for Energy, Design for Speed, or Design for Security. Design for Security is essentially the Security and Privacy by Design (SPbD) which was the theme of March 2020 issue of IEEE MCE. We dedicated cover of April 2017 issue of IEEE MCE to deep learning aka deep neural network (DNN). In September 2019 issue of IEEE MCE, we addressed edge-AI, in which AI at the edge devices (close to the user) was highlighted. The current issue (May 2020) of IEEE MCE further advances these efforts on AI. AI is the superset covering machine learning (ML), expert system, and computational intelligence. A subset of AI is machine learning (ML) and a subset of ML is deep learning (DL). Computational intelligence includes artificial neural network (ANN), and a subset of which is deep neural network (DNN).","PeriodicalId":179001,"journal":{"name":"IEEE Consumer Electron. Mag.","volume":"7 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Consumer Electron. Mag.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/mce.2020.2968754","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
It reminds me that IEEE MCE has covered intelligent electronics, smart electronics kind articles in some of its past issues. In addition, I guest edited a special issue on smart electronics in IEEE Potentials. In Jan 2019 issue of IEEE Potentials, I defined smart electronics as the class C E systems that are envisioned to be Energy-Smart, Security-Smart, and Response-Smart. I advocated that these 3 key aspects and design trade-offs among them is the crucial for the next generation CE. In fact, in my booked titled “Nanoelectronic Mixed-Signal Systems” published in 2015, I presented a broad perspective for design trade-offs of CE systems under the theme “Design of Excellence (DFX)” or ‘Design of X (DFX)”. In DFX, “X” refers to a subset of characteristics/figures-of-merit (FoMs), such as energy, speed, security, and safety, making it Design for Energy, Design for Speed, or Design for Security. Design for Security is essentially the Security and Privacy by Design (SPbD) which was the theme of March 2020 issue of IEEE MCE. We dedicated cover of April 2017 issue of IEEE MCE to deep learning aka deep neural network (DNN). In September 2019 issue of IEEE MCE, we addressed edge-AI, in which AI at the edge devices (close to the user) was highlighted. The current issue (May 2020) of IEEE MCE further advances these efforts on AI. AI is the superset covering machine learning (ML), expert system, and computational intelligence. A subset of AI is machine learning (ML) and a subset of ML is deep learning (DL). Computational intelligence includes artificial neural network (ANN), and a subset of which is deep neural network (DNN).