Edge AI research watch
Edge AI compresses the distance between models and the real world. That improves latency and privacy, but it also gives attackers more direct physical access to the device and the implementation.
Why it matters
Edge deployment makes implementation details first-class security concerns. Power traces, fault behavior, memory exposure, and sensor access all matter in ways that are invisible in purely cloud-based evaluation.
How to use this page
Use this page to comment on papers or trends related to embedded inference, AI accelerators, device trust, or hardware-aware attack models. These notes can later be promoted into bigger survey sections.
Useful prompts for future updates
Suggested post prompts: What changes when the attacker can touch the device? Which defenses remain practical under power and area limits? What is missing from current benchmarks?