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A technical guide to real-time edge video AI under latency and power limits. It covers inference cadence, backpressure, deployment reliability, and systems tradeoffs.
A guide to secure OTA design for embedded and AI devices. It covers signatures, staged rollout, rollback rules, recovery paths, and trust-preserving delivery.
A practical guide to shipping embedded AI on devices that live with real constraints. It covers model footprint, timing, watchdogs, updates, and operational reliability.
A practical guide to private AI on mobile and edge devices. It covers on-device inference, data minimization, update strategy, and safeguards for sensitive workloads.
