Tiny machine learning (tinyML) is a fast-growing field of machine learning technologies enabling on-device sensor data analytics at extremely low power, typically in the milliwatt range and below. The tinyML ecosystem is fueled by (i) emerging commercial applications and new systems concepts on the horizon; (ii) significant progress on algorithms, networks, and models down to 100 kB and below; and (iii) current low-power applications in vision and audio that are already becoming mainstream and commercially available. There is growing momentum demonstrated by technical progress and ecosystem development in all of these areas. The tinyML research symposium serves as a flagship venue for related research at the intersection of machine learning applications, algorithms, software, and hardware in deeply embedded machine learning systems.

The tinyML Research Symposium is held in conjunction with the tinyML Summit, the premier annual gathering of senior level technical experts and decision makers representing fast growing global tinyML community.

Call for Papers

  • We solicit papers from academia and industry that emphasize cross-layer innovation in the field of tinyML
  • Submissions must intersect and leverage synergy between at least two of the subject areas listed below
  • An author of an accepted paper must attend the symposium in person to give a presentation
  • Accepted full papers will be published as peer-reviewed online proceedings on arXiv. In addition, the authors of accepted full papers will be invited to submit an extended manuscript to a Special Issue in ACM’s Transactions on Embedded Computing Systems (TECS).

Submission Format

Subject Areas

  • tinyML Datasets: Public release of new datasets to tinyML; frameworks that automate dataset development; survey and analysis of existing tiny datasets that can be used for research

  • tinyML Applications: Novel applications across all fields and emerging use cases; discussions about real-world use cases; user behavior and system-user interaction; survey on practical experiences

  • tinyML Algorithms: Federated learning or stream-based active learning methods; deep learning and traditional machine learning algorithms; pruning, quantization, optimization methods; security and privacy implications

  • tinyML Systems: Profiling tools for measuring and characterizing performance and power; solutions that involve hardware and software co-design; characterization of tiny real-world embedded systems; in-sensor processing, design, and implementation

  • tinyML Software: Interpreters and code generator frameworks for tiny systems; optimizations for efficient execution; software memory optimizations; neural architecture search methods

  • tinyML Hardware: Power management, reliability, security, performance; circuit and architecture design; ultra-low-power memory system design; MCU and accelerator architecture design and evaluation

  • tinyML Evaluation: Measurement tools and techniques; benchmark creation, assessment and validation; evaluation and measurement of real production systems