TERMon: Detecting Persistent Behavioral Threats in Edge AI via Hardware-Native Ternary Runtime Monitor
TERMon, an FPGA runtime monitor, flags harmful weight corruption in edge AI using ternary behavioral fingerprints.
TERMon is a lightweight FPGA runtime monitor that fingerprints trusted class-conditional inference behavior as ternary patterns and matches them against a thermometer-encoded trace. On a PYNQ-Z2, the pipelined design uses no block RAM or DSPs and decides in two cycles. It detects harmful weight corruptions in proportion to behavioral impact, but out-of-distribution and adversarial inputs were largely not separable at a strict false-positive point. The ternary encoding is designed to match the corresponding unquantized range decision exactly.
- TERMon matches ternary trusted-behavior fingerprints during edge AI inference.
- PYNQ-Z2 implementation needs no BRAM or DSPs and decides in two cycles.
- Harmful weight corruption is detected in proportion to behavioral impact.
- Adversarial and out-of-distribution inputs were mostly not separable.
From the original article
1 min read at arxiv.orgEdge AI accelerators are increasingly deployed in safety-critical environments, where model outputs may control physical actuators, make access-control decisions, or trigger alarms. In these settings, runtime failures often remain undetected because model corruption, distribution shift, and adversarial inputs can still produce well-formed, confident predictions.
By Arish Sateesan · arxiv.org
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