arXiv AI
Can Power Draw Constrain Covert Compute? Limits of Analogue Verification for AI Governance
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
arXiv AI
Sep 2
Workload Identification with Physical Side Channels for AI Governance
The paper demonstrates that an external observer can identify the type of workload running on an NVIDIA H200 GPU by analyzing its power draw, distinguishing training, inference, and non‑AI tasks with high accuracy. Using 930 recorded traces, the authors achieve 97% accuracy and a macro‑averaged F1 score of 0.955 on unseen model families. They also test four evasion strategies to disguise training as inference, showing that a hardened detector can catch most attacks, though one strategy (LoRA) remains partially detectable.
arXiv Machine Learning
Jun 2
Bit-Exact AI Inference Verification Without Performance Tradeoffs
arXiv:2606. 00279v1 Announce Type: cross Abstract: Verifying claims about AI workloads is a pre- requisite for credible AI governance of covert adversaries (who comply with monitoring only when detection likelihood is high), yet the ap- parent non-determinism of GPU floating-point arithmetic forces auditors to accept approximate output matches.
arXiv Machine Learning
Oct 23
Detecting AI Training in Datacentre Power
The paper proposes a method to detect large AI model training runs on a data‑centre power grid using Viterbi tracking of iteration cadence. On simulated data, the tracker identifies at least 96 % of training runs with a 1 % false‑alarm rate when the operator does not hide activity, and remains effective even against several adversarial hiding strategies. The approach requires only a modest passive power meter sampling at 2 Hz or faster, making hidden training both detectable and costly for the operator.
arXiv AI
Jun 16
The Energy Blind Spot: NVIDIA's Flagship Edge AI Hardware Cannot Support Process-Level Energy Attribution
arXiv:2605. 27599v2 Announce Type: replace-cross Abstract: Agentic AI workloads - where a single user goal triggers multi-step orchestration, tool calls, retries, and failure recovery - are being targeted for edge deployment, with NVIDIA, Dell, HP, ASUS, MSI, Acer, and Gigabyte all shipping GB10-based desktop AI systems in 2026.
arXiv AI
Oct 31
LLM-PowerTrace: A Dataset of Time-Resolved GPU Power Traces for AI Compute
LLM-PowerTrace is an open dataset that provides time‑resolved, per‑GPU power traces for large language model fine‑tuning and inference. The traces are sampled every 100 ms across various NVIDIA platforms and include detailed workload and system metadata. The dataset covers a wide range of model families, sizes, and controlled sweeps over batch size, sequence length, precision, parallelism, checkpointing, concurrency, GPU frequency, KV‑cache and weight quantization, prompt and output lengths, and request‑arrival patterns, and it defines power‑grid‑facing metrics and an AI‑compute power & energy scaling law.
arXiv AI
Jul 16
Audited Selective Verification for Risk-Controlled N-1 Thermal Contingency Screening under Deployment Shift
arXiv:2607. 13221v1 Announce Type: cross Abstract: Real-time N-1 contingency screening in an energy management system trades assurance against cost: verifying every credible outage with full power flow is too slow, while fast linear-sensitivity screening gives no statistical guarantee and can silently pass unsafe operating points, especially when a controller drives the system into unfamiliar regimes.