arXiv:2607. 15328v1 Announce Type: cross Abstract: Complex algorithms such as deep neural networks are increasingly being deployed on embedded, resource constrained platforms.
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The paper introduces REQAP, a reliability‑aware quantized weight packing technique for systolic‑array DNN accelerators. It uses a sensitivity‑driven mixed‑precision quantization to assign layer‑wise bit‑widths, a deterministic register‑level packing strategy for SIMD‑within‑a‑register execution, and selective bit‑level protection that replicates critical MSBs into unused register space. Experiments on AlexNet, VGG‑11, and ResNet‑18 show up to 62% memory reduction, 56% fewer MAC operations, and improved accuracy resilience under fault injection compared to baseline and fully protected models.
arXiv:2602. 23334v2 Announce Type: replace-cross Abstract: Neural network accelerators have been widely applied to edge devices for complex tasks like object tracking, image recognition, etc.
arXiv:2603. 22770v2 Announce Type: replace-cross Abstract: The deployment of deep neural networks (DNNs) in safety-critical edge environments necessitates robustness against hardware-induced bit-flip errors.
arXiv:2605. 24391v2 Announce Type: replace-cross Abstract: As the demand for deep learning grows, cost reduction through quantization has become essential for both training and inference.
arXiv:2510. 16028v4 Announce Type: replace-cross Abstract: Neural networks increasingly run on hardware outside the user's control (cloud GPUs, inference marketplaces).
arXiv:2608. 06916v1 Announce Type: new Abstract: Quantized Neural Networks~(QNN) with low-bitwidth data have proven promising in efficient storage and computation on edge devices.