Tiny machine learning is broadly defined as a fast growing field of machine learning technologies and applications including hardware (dedicated integrated circuits), algorithms and software capable of performing on-device sensor (vision, audio, IMU, biomedical, etc.) data analytics at extremely low power, typically in the mW range and below, and hence enabling a variety of always-on use-cases and targeting battery operated devices. The inaugural tinyML Summit in March 2019 showed very strong interest from the community with active participation of senior experts from 90 companies. It revealed that: (i) tiny machine learning capable hardware is becoming “good enough” for many commercial applications and new architectures (e.g. in-memory compute) are on the horizon; (ii) significant progress on algorithms, networks and models down to 100kB and below; and (iii) initial low power applications in the vision and audio space. There is growing momentum demonstrated by technical progress and ecosystem development.
tinyML Summit 2020 will cover the whole stack of technologies (Systems-Hardware-Algorithms-Software-Applications) at the deep technical levels. While the majority of the participants and speakers will come from industry, leading edge academic research will be represented as well as an important ingredient of the evolving tiny machine learning ecosystem. In 2020, special attention will be given to recent progress on algorithm development and tiny machine learning use-cases and applications. The program will be organized in four technical sessions: Hardware, Systems, Algorithms & Software, and Applications. There will be approximately twenty invited presentations selected by the Technical Program Committee and dedicated poster sessions and demos by tiny machine learning companies and sponsors. Overview and hands-on tutorials on hardware and software developments will be available the day before the main technical program starts.
February 12-13 2020, San Jose, US - Registrations at https://tinymlsummit.org