Melting machine learning with in-sensor computing
Technical Director, IEEE & ST Fellow - System Research and Applications
Nowadays, we are experiencing mode sophisticated machine learning models such as Minerva, PaLM, GPT-3 somehow regardless of the complexity they feature. These models poses some hard questions: how much energy is it required to train them? how can they scale across four billion android users? Is there any limit to model hyper-parametrization? how to avoid data contamination? what is the proper training data vs parameter ration? Is this approach sustainable for the future of the planet?
For the experts on embedded computing, the obvious counter action to this trend is to look for tiny machine learning solutions. Indeed, since 2019 TinyML Foundation and MLCommons created a vibrant community focused on developing low power devices with open benchmarks mainly concentrating on micro-controllers and neural processing units. Unfortunately, sensor devices were poorly considered as execution targets because of their extreme specific properties. One shall look to them not with a “more Moore” opportunity and vice versa with a mindset “less is more”. With that in mind, this talk will review a couple of sensors which are aimed to push forward the tiny concept to the extreme low boundary both in term of power consumption, die area and accuracy. Two examples will be elaborated about in sensor machine learning computing: inertial and pressure sensor with two different computing paradigms.
Danilo Pau (h-index 25, i10-index 65) graduated in 1992 at Politecnico di Milano, Italy. One year before his graduation, he joined SGS-THOMSONS (now STMicroelectronics) as interns on Advanced Multimedia Architectures, and he worked on memory reduced HDMAC HW design. Then MPEG2 video memory reduction. Next, on video coding, transcoding, embedded 2/3 graphics, and computer vision. Currently, his work focuses on developing solutions for tiny machine learning tools.
Since 2019 Danilo is an IEEE Fellow; he served as Industry Ambassador coordinator for IEEE Region 8 South Europe, was vice-chairman of the “Intelligent Cyber-Physical Systems” Task Force within IEEE CIS, was IEEE R8 AfI member in charge of internship initiative. Today he is a Member of the Machine Learning, Deep Learning and AI in the CE (MDA) Technical Stream Committee CESoc. He was AE of IEEE TNNLS. He wrote the IEEE Milestone on Multiple Silicon Technologies on a chip, 1985 which was ratified by IEEE BoD in 2021 and IEEE Milestone on MPEG Multimedia Integrated Circuits, 1984-1993 which was ratified in 2022. He served as TPC member to TinyML EMEA forum and is the chair of the TinyML On Device Learning working group. He serves as 2023 IEEE Computer Society Fellow Evaluating Committee Members.
With over 83 application patents, 150 publications, 113 MPEG authored documents and 66 invited talks/seminars at various Universities and Conferences, Danilo's favorite activity remains supervising undergraduate students, MSc engineers and PhDs.