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Found 6 papers, 1 papers with code
ProSparse: Introducing and Enhancing Intrinsic Activation Sparsity within Large Language Models
Some recent efforts have explored introducing ReLU or its variants as the substitutive activation function to help LLMs achieve activation sparsity and inference acceleration, but few can simultaneously obtain high sparsity and comparable model performance.
Pinpointing the Memory Behaviors of DNN Training
The training of deep neural networks (DNNs) is usually memory-hungry due to the limited device memory capacity of DNN accelerators.
Fusion-Catalyzed Pruning for Optimizing Deep Learning on Intelligent Edge Devices
The increasing computational cost of deep neural network models limits the applicability of intelligent applications on resource-constrained edge devices.
LANCE: Efficient Low-Precision Quantized Winograd Convolution for Neural Networks Based on Graphics Processing Units
Accelerating deep convolutional neural networks has become an active topic and sparked an interest in academia and industry.
Background subtraction on depth videos with convolutional neural networks
Background subtraction is a significant component of computer vision systems.
Auto-tuning Neural Network Quantization Framework for Collaborative Inference Between the Cloud and Edge
By analyzing the characteristics of layers in DNNs, an auto-tuning neural network quantization framework for collaborative inference is proposed.
