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Only Papers#On device
Nov 13Fri
  1. Hacker News AI36

    Air-LLM: Broadcasting AI Weights for Edge LLM Inference via RF Computing

    The paper proposes AIR-LLM, an edge LLM inference architecture in which a central radio such as a 5G base station broadcasts LLM weights over the air and edge users perform the general matrix-vector multiplication of inference directly in the RF domain using RF mixers, avoiding weight storage and loading. It uses MIMO spatial multiplexing with an energy-efficient precoder-postcoder pair to shorten airtime, and because the central radio stays user-unaware, one broadcast can serve unlimited users in coverage. Evaluated on NVIDIA Sionna ray-traced channels of two urban scenes with a real RF mixer profile, AIR-LLM shows 4.0% WikiText-2 perplexity degradation on LLaMA-3.1-8B while saving 157.7x/40.4x energy versus FP16 and weight-only quantization baselines, and with 20 users its airtime is 104.1x/26.0x shorter.

Nov 4Wed
  1. Hacker News AI31

    EdgeDAE: Acceleration of Diffusion Action Experts for Real-Time Physical AI

    EdgeDAE is a heterogeneous FPGA-GPU system that offloads the perception-heavy vision transformer to the GPU while accelerating Diffusion Action Expert (DAE) inference on an FPGA by storing all parameters on-chip in BRAM/URAM, eliminating the repeated DRAM parameter loading of iterative denoising. It co-designs quantization, fixed-point arithmetic, and hardware-efficient random number generation for the FPGA fabric. Compared with an edge GPU baseline, it reduces end-to-end inference latency by 52.5% for Octo-Small and 38.7% for Octo-Base with up to 2.10x higher throughput, and achieves about 17x higher energy efficiency than an RTX 4090.

  2. Simon Willison50

    Qwen 3.8 27B on my laptop can interpret model weights, too

    Simon Willison tested the "Sparks of Weight-Space Programming" paper's game-identification prompts, which ask a model to name which of four Atari-style games a policy network was trained on using only its raw weights and no tools. Running Qwen 3.8 27B locally via LM Studio, he got correct answers for Pac-Man (46m 12s, 43,506 reasoning tokens), Pong (1h 4m 54s) and Breakout (52m 25s), but it failed on Freeway. He notes critics like Matthew Honnibal and Sean Goedecke dispute the paper's framing, and he focuses on the no-tools question rather than claims that source code is obsolete.