首页|Reports from University of Verona Add New Study Findings to Research in Machine Learning (A Machine Learning-Oriented Survey on Tiny Machine Learning)
Reports from University of Verona Add New Study Findings to Research in Machine Learning (A Machine Learning-Oriented Survey on Tiny Machine Learning)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New study results on artificial intell igence have been published. According to news reporting out of Verona, Italy, by NewsRx editors, research stated, "The emergence of Tiny Machine Learning (TinyM L) has positively revolutionized the field of Artificial Intelligence by promoti ng the joint design of resource-constrained IoT hardware devices and their learn ing-based software architectures." Funders for this research include European Commission. The news editors obtained a quote from the research from University of Verona: " TinyML carries an essential role within the fourth and fifth industrial revoluti ons in helping societies, economies, and individuals employ effective AI-infused computing technologies (e.g., smart cities, automotive, and medical robotics). Given its multidisciplinary nature, the field of TinyML has been approached from many different angles: this comprehensive survey wishes to provide an up-to-dat e overview focused on all the learning algorithms within TinyML-based solutions. The survey is based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodological flow, allowing for a systematic and compl ete literature survey. In particular, firstly, we will examine the three differe nt workflows for implementing a TinyML-based system, i.e., ML-oriented, HW-orien ted, and co-design. Secondly, we propose a taxonomy that covers the learning pan orama under the TinyML lens, examining in detail the different families of model optimization and design, as well as the state-of-the-art learning techniques. T hirdly, this survey will present the distinct features of hardware devices and s oftware tools that represent the current state-of-the-art for TinyML intelligent edge applications."
University of VeronaVeronaItalyEur opeCyborgsEmerging TechnologiesMachine LearningSoftware
NETL
NSTL
