Scaling AI at the Edge: Engineering for Next-Generation Embedded Systems
As artificial intelligence moves from the cloud to embedded systems, engineering teams face unique challenges in deploying and maintaining AI-enabled devices at scale.
Join us for an in-depth discussion with LeafLabs, whose engineers have developed embedded systems for environments ranging from deep sea to space, as they share practical insights from their work at the intersection of AI and embedded systems.
In this technical panel discussion, we'll explore the real-world challenges of implementing AI in resource-constrained environments, from selecting the right hardware architecture to managing firmware updates for AI models in the field. LeafLabs will share their experience balancing competing demands of processing power, battery life, and reliable operation, while addressing critical security considerations for AI-enabled devices.
Key Topics Include:
Bridging the gap between AI development and embedded deployment
Making informed architecture decisions: hardware selection, compute location (edge vs. cloud), and security implementation
Questioning assumptions and understanding the "why" behind technical requirements
Managing updates for AI models and firmware across device fleets
Optimizing resources: balancing power consumption, memory management, and cost considerations
Leveraging platforms like Peridio to decrease engineering costs and simplify maintenance
Whether you're working on your first AI-enabled product or scaling an existing fleet of devices, this session will provide valuable insights you won’t want to miss.
Panelists
Bill Brock - CEO @ Peridio
Justin Schneck - CPO @ Peridio
Jeff Ciesielski - Director of Research @ LeafLabs
Who Should Attend:
Firmware Development Teams
DevOps Engineers
Product Managers
Quality Assurance Teams
Technical Decision Makers
Join us to discover the strategies and tools that are helping leading organizations ship firmware updates with confidence and predictability.
