Artificial Intelligence is increasingly moving to the edge, powering devices such as smartwatches, home automation systems, and autonomous vehicles. These devices demand real-time, privacy-preserving intelligence under strict constraints of compute, memory, and energy. TinyML addresses this challenge by creating lightweight models optimised for resource-limited environments, yet maintaining accuracy and reliability remains a significant hurdle amid dynamic conditions and diverse data.
Neuromorphic computing offers a complementary approach, inspired by the human brain, enabling event-driven, massively parallel processing with ultra-low power consumption. By combining TinyML’s compact learning models with neuromorphic systems’ non-von Neumann architecture, we can unlock scalable, adaptive, and energy-efficient solutions for edge intelligence.
This workshop brings together researchers and practitioners to explore this convergence, tackle technical challenges, and share innovations that make intelligent edge systems practical, sustainable, and cost-effective. Our goal is to foster collaboration and drive breakthroughs in methodologies that redefine AI at the edge.
Day 1
Registration 8. a.m.–9.15 a.m.
9.30 a.m.–10.15 a.m.
Dr. Nitin Chawla (STMicroelectronics)
10.15 a.m.–11 a.m.
[Speaker name]
Break 11 a.m.–11.30 p.m.
11.30 a.m.–12 p.m.
[Speaker name]
12 p.m.–12.30 p.m.
Name of activity lead
12.30 p.m.–1 p.m.
Name of speaker
Lunch 1 p.m.–2 p.m.
2 p.m.–2.30 p.m.
[Speaker name]
2.30 p.m.–3 p.m.
[Speaker name]
Break 3 p.m.–4 p.m.
4 p.m.–4.30 p.m.
[Speaker name]
4.30 p.m.–5 p.m.
[Speaker name]
Party at [Venue name] 6 p.m.–9 p.m.