Introduces the first heterogeneous multi-source mmWave point cloud HAR dataset and DAP-Net architecture with Doppler reparameterization and text alignment for cross-source robustness.
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LLM.int8() performs 8-bit inference for transformers up to 175B parameters with no accuracy loss by combining vector-wise quantization for most features with 16-bit mixed-precision handling of systematic outlier dimensions.
SURGE proposes a dual-path gradient compensator and adaptive scaler to learn better surrogate gradients for binary neural network training, outperforming prior methods on classification, detection, and language tasks.
LBLLM achieves better accuracy than prior binarization methods for LLMs by decoupling weight and activation quantization through initialization, layer-wise distillation, and learnable activation scaling.
HTAF is a sigmoid-tanh composite that approximates the Heaviside function to allow stable gradient training of binary activation networks, yielding ICBMs with stable discretization and competitive performance on image tasks.
A BNN-based YOLOv3-tiny-like object detector with 1-bit weights and 8-bit activations is implemented in Verilog on FPGA, achieving 39.6% mAP50 on VOC and 0.999964 correlation with the ONNX model in RTL simulation.
citing papers explorer
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DAP: Doppler-aware Point Network for Heterogeneous mmWave Action Recognition
Introduces the first heterogeneous multi-source mmWave point cloud HAR dataset and DAP-Net architecture with Doppler reparameterization and text alignment for cross-source robustness.
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LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale
LLM.int8() performs 8-bit inference for transformers up to 175B parameters with no accuracy loss by combining vector-wise quantization for most features with 16-bit mixed-precision handling of systematic outlier dimensions.
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SURGE: Surrogate Gradient Adaptation in Binary Neural Networks
SURGE proposes a dual-path gradient compensator and adaptive scaler to learn better surrogate gradients for binary neural network training, outperforming prior methods on classification, detection, and language tasks.
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LBLLM: Lightweight Binarization of Large Language Models via Three-Stage Distillation
LBLLM achieves better accuracy than prior binarization methods for LLMs by decoupling weight and activation quantization through initialization, layer-wise distillation, and learnable activation scaling.
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A Composite Activation Function for Learning Stable Binary Representations
HTAF is a sigmoid-tanh composite that approximates the Heaviside function to allow stable gradient training of binary activation networks, yielding ICBMs with stable discretization and competitive performance on image tasks.
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Design and Implementation of BNN-Based Object Detection on FPGA
A BNN-based YOLOv3-tiny-like object detector with 1-bit weights and 8-bit activations is implemented in Verilog on FPGA, achieving 39.6% mAP50 on VOC and 0.999964 correlation with the ONNX model in RTL simulation.