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Only Research#On-device
TodayNovember 13Friday1 items
  1. Hacker News · AI31

    Air-LLM: Broadcasting AI Weights to Edge Devices via RF Computing for On-the-Fly LLM Inference

    Air-LLM is an edge LLM inference architecture in which a central radio such as a 5G base station broadcasts model weights over the air, and edge users complete GEMV directly in the RF domain using RF mixers, so devices never store or load weights. Tested on NVIDIA Sionna ray-traced channels in two real urban scenes with a real RF mixer profile, it limits WikiText-2 perplexity degradation on LLaMA-3.1-8B to 4.0% while saving 157.7x/40.4x energy versus FP16 and weight-only quantization baselines. With 20 users, airtime is 104.1x/26.0x shorter, and because the central radio stays user-unaware, one broadcast serves unlimited users in coverage.

November 6Friday
  1. Google Research72

    Google researchers detect hypertension signs with smartwatch PPG and accelerometer data, no cuff calibration needed

    Google Research published a Nature study on AI-PPG-ACC-HTN, a multimodal system that detects signs of hypertension from smartwatch PPG and accelerometer data without cuff calibration. Validated against 24-hour ambulatory blood pressure monitoring in 196 participants across five US sites, it reached 65.8% sensitivity and 90.0% specificity for initial screening, and 78.4% sensitivity and 90.9% specificity for confirmatory testing. The authors describe the work as early feasibility research, not a replacement for clinical arm cuffs or a standalone diagnosis.

    Why it matters: Google's cuffless smartwatch hypertension model is benchmarked against 24-hour ambulatory monitoring, showing how passive multi-day sensor aggregation could support opportunistic screening.