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Precomputed 1D-CNNs for Atrial Fibrillation Detection on Tiny Smart Sensor Systems
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PALUTE: Processing-In-Memory Acceleration via Lookup Table for Edge LLM Inference
arXiv:2606.08891v1 Announce Type: new Abstract: Large language models are increasingly deployed on edge devices with tight power and area budgets. While mixed-precision GEMM reduces arithmetic complexity, quantized inference is often dominated by dequantization and nonlinear operators. Lookup Table (LUT)-based method mitigates these costs by precomputing outputs and replacing repeated arithmetic with table lookups, but existing designs incur significant capacity and lookup-latency overheads.
Steering Vectors are an Adversarial Attack Surface
Announce Type: new Abstract: Activation steering has become a popular way to control Large Language Model (LLM) behavior without fine-tuning. Since the technique is plug-and-play, users share datasets and precomputed vectors to steer model activations. However, we show that a \emph{stealth data poisoning attack} silently compromises this pipeline.
Hard Labels In! Rethinking the Role of Hard Labels in Mitigating Local Semantic Drift
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Neural Spectral Element Methods for stiff multiphysics PDEs with electrochemical transport benchmarks
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Bidirectional Incremental Generalized Hybrid A*
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QCFuse: Query-Aware Cache Fusion via Compressed View for Efficient RAG Serving
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CoPark: Learning Reactive Parking via Self-Play
arXiv:2606.04149v1 Announce Type: new Abstract: Learning a single policy that reaches a goal with high geometric precision while interacting safely with nearby agents poses conflicting objectives. Precision favors commitment to a fixed geometric plan, whereas interaction requires immediate deviation when another agent intrudes, causing policies optimized for one objective to often fail at the other.
Ultrafast machine learning on FPGAs via Kolmogorov-Arnold Networks
Ultrafast machine learning on FPGAs via Kolmogorov-Arnold Networks This post is a high-level explainer for my Master’s thesis, which involves designing hardware architectures for ultrafast inference and online learning using the Kolmogorov-Arnold Network (KAN) architecture. I’ll assume familiarity with standard machine learning concepts, as well as some understanding of hardware and digital circuits; read my previous post here for the latter. Please read the two papers below for more...