**Note: The talks hosted by TinyML Italy will be in Italian
**Nota: I talk presentati da TinyML Italia saranno in Italiano
TinyML is attracting interest across various industries for the massive opportunity in bringing machine learning to low-power devices, such as microcontrollers. This technology is already around us and promising to make AI ubiquitous. For example, TinyML is improving automation and resource efficiency usage in industrial and agriculture fields and allowing the monitoring of our health with smartwatches, just to name a few. By nature, TinyML is a multidisciplinary technology. Therefore, we require knowledge from different domains to build effective TinyML-based applications. Hence, where do we start, and what do we need to know to be experts in this field? In this presentation, we will address these questions and not only. We will start by explaining what TinyML is and why it is now an attractive field. The second part will be dedicated to the HW and SW components at the heart of this technology. We will analyze their role and their interaction when building TinyML solutions.
The third part will highlight the challenges when deploying TinyML applications on memory-constrained devices.
In the end, we will present a TinyML demo with Edge Impulse on Arduino Nano 33 BLE Sense board and give an overview of the upcoming TinyML book.
TinyML sta attirando sempre piú interesse in vari settori per l’enorme opportunità di accelerare ML su dispositivi low-power, come i microcontrollori. Questa tecnologia è già intorno a noi e promette di rendere l’Intelligenza Artificiale (AI) ubiquita. Ad esempio, TinyML e’ gia impiegata sia in ambito industriale che agricolo per migliorare l’automazione e l’efficienza di utilizzo di risorse. TinyML, per sua natura, è una tecnologia multidisciplinare; pertanto, se siamo alle prime armi, da dove possiamo iniziare e cosa dobbiamo conoscere per diventare esperti in questo campo? Attraverso questa presentazione daremo una risposta a tali domande e non solo. Inizieremo spiegando cos’è TinyML e perché adesso ha attratto interesse in diversi settori. La seconda parte sarà dedicata ai componenti HW e SW alla base di questa tecnologia dove analizzeremo il ruolo di ognuno di questi e la loro interazione durante lo sviluppo di una soluzione TinyML.
La terza parte mostrera’ le sfide da considerare nel momento in cui realizziamo applicazioni di TinyML su dispositivi con poca memoria.
Infine, presenteremo una demo con Edge Impulse su Arduino Nano 33 BLE Sense e forniremo una panoramica del prossimo libro di TinyML.
Schedule
Timezone: PST
TinyML: A practical analysis of the technology that will make AI ubiquitous
Gian Marco IODICE, Team and Tech Lead in the Machine Learning Group
Arm
Gian Marco IODICE, Team and Tech Lead in the Machine Learning Group
Arm
Gian Marco Iodice is the team and tech lead in the Machine Learning Group at Arm, for the Arm Compute Library project.
Gian Marco was behind the development of the Arm Compute Library from the very beginning, and with several years of experience in the field of development and optimization of machine learning and computer vision on embedded devices, Gian Marco is now leading the ML performance optimization software team on Arm Mali GPUs and Arm Cortex-A CPUs.
He received the MSc degree, with honours, in electronic engineering from the University of Pisa (Italy) where he specialized in SW/HW Co-design. In the last few years Gian Marco has been a frequent speaker at Embedded Vision Summit where he presented optimization techniques and design solutions for CNNs.
In 2020, Gian Marco co-founded the TinyML UK group with Dominic Binks, Alessandro Grande and Neil Cooper.
Schedule subject to change without notice.