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This paper explores the development of microcredentials (MCs) through a regional lens, considering their implications for lifelong learning and skilled labour mobility in Asia and the Pacific. It also examines recent global initiatives led by the United Nations Educational, Scientific and Cultural Organization (UNESCO).

The study reviews recent global initiatives aimed at developing shared definitions and principles for MCs, as led by UNESCO, taking into account the importance of existing initiatives such as the pilot project launched by the Australian government in 2022. The formalization of MCs within national education systems is also examined, with reference to frameworks developed by organizations like the European Union (EU), Australia and the Malaysia Qualifications Agency (MQA, 2020).

The paper underscores the increasing attention from international organizations and governments to the development and recognition of MCs, with significant efforts in countries like Australia and pilot initiatives in New Zealand and Malaysia. However, it also highlights that many MC projects in Asia are still in their early developmental stages, emphasizing the need for further progress and formalization.

This paper offers a unique perspective on the development of MCs in the Asia–Pacific region and their potential role in international education. It explores the prospects for MCs in international education, including student mobility and cross-border online courses. The challenge of integrating MCs into existing systems for recognizing international qualifications is discussed. Overall, the paper contributes to the understanding of MC frameworks as essential tools in the formalization of lifelong learning in the region, supported by the provided citations (OECD, 2021).

This paper aims to review in-memory computing (IMC) for machine learning (ML) applications from history, architectures and options aspects. In this review, the authors investigate different architectural aspects and collect and provide our comparative evaluations.

Collecting over 40 IMC papers related to hardware design and optimization techniques of recent years, then classify them into three optimization option categories: optimization through graphic processing unit (GPU), optimization through reduced precision and optimization through hardware accelerator. Then, the authors brief those techniques in aspects such as what kind of data set it applied, how it is designed and what is the contribution of this design.

ML algorithms are potent tools accommodated on IMC architecture. Although general-purpose hardware (central processing units and GPUs) can supply explicit solutions, their energy efficiencies have limitations because of their excessive flexibility support. On the other hand, hardware accelerators (field programmable gate arrays and application-specific integrated circuits) win on the energy efficiency aspect, but individual accelerator often adapts exclusively to ax single ML approach (family). From a long hardware evolution perspective, hardware/software collaboration heterogeneity design from hybrid platforms is an option for the researcher.

IMC’s optimization enables high-speed processing, increases performance and analyzes massive volumes of data in real-time. This work reviews IMC and its evolution. Then, the authors categorize three optimization paths for the IMC architecture to improve performance metrics.