Only 70 years after the birth of the world's first computer ENIAC, computer technologies have reached the level of defeating a professional human Go player. On the other hand, computers still have points that are significantly inferior to the human brain. One of them is “energy efficiency”. In this paper, the path that computers have taken from the perspective of energy efficiency is clarified and current issues and research activities are described. In addition, approximate computing techniques to improve energy efficiency are discussed and research cases in my laboratory are also introduced.

This paper deals with the Shannon cipher system, secret sharing communication systems, secret sharing systems, secure network coding, and wiretap channel coding, which are tools for the secure coding of information based on information theory. In coding problems, it is usually studied how to achieve the perfect secrecy of information. However, in this paper, we show that we can achieve secure coding even in the case of no-perfect secrecy. We divide secret information into several pieces, and we achieve perfect secrecy for every piece individually to insure the security of information. On the other hand, by allowing the nonperfect secrecy of the whole information, we can decrease the bit length of random number and/or codewords. Hence, the coding techniques shown in this paper attain secure and efficient coding in the above coding systems.

Amplitude death has been intensively investigated in the field of nonlinear science for almost 40 years. In the present article, we review the use of analytical tools for robust stability/control, which have been provided in the field of system control, to obtain the conditions under which amplitude death occurs when the number of oscillators and the network topologies are unknown. Specifically, we explain our results for two representative couplings for inducing amplitude death, dynamical coupling and delayed coupling.

Machine learning models of high predictive performance, such as deep neural networks and ensemble models, now play a central role in the current artificial intelligence technologies and have started to be applied to the problems related to our health or properties. However, one of the primary obstacles here is the opacity of such high-performance models. So far, dozens of techniques for reducing the opacity have been explored, and form a research field called “explainable aritificial intelligence (XAI).” In this paper, I review the past literature on XAI, organize key concepts and techniques in the current XAI research, and discuss the future direction of XAI.

Authenticity under threat is a severe issue in modern information, communication and technology (ICT) electronics. Counterfeit electronic components, particularly semiconductor integrated circuit (IC) chips, become the known problems of compromise in the reliability and safety of such ICT devices. In this report, we try to explain the general trends of supply chain security from the viewpoints of IC chip authenticity and potential hardware Trojan problems in IC chips.

With the arrival of the big data era, the amount of digital content on the network has rapidly increased. The use of edge/cloud computing has become widespread in various fields such as big data analysis. Examples include data compression to reduce the amount of data, data mining to extract significant information from data, and machine learning to automatically learn models for the classification and prediction of data. However, the use of edge/cloud computing is premised on the reliability of service providers, and there is a concern that privacy invasion problems may occur as a result of the lack of reliability and the unauthorized use or loss of data owing to accidents. To solve the problems, privacy-preserving sparse modeling based on the random unitary transform has been proposed. The features of privacy-preserving sparse modeling are that 1) it can process encrypted data without changing the algorithm of sparse modeling and 2) it guarantees no degradation of sparse modeling performance, without a significant increase in the amount of calculation. In this article, we overview the basic mechanism of the privacy-preserving sparse modeling and introduce application examples to edge AI.