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Found 7 papers, 2 papers with code
Accelerating 3D Gaussian Splatting with Neural Sorting and Axis-Oriented Rasterization
3D Gaussian Splatting (3DGS) has recently gained significant attention for high-quality and efficient view synthesis, making it widely adopted in fields such as AR/VR, robotics, and autonomous driving.
Accelerating Prefilling for Long-Context LLMs via Sparse Pattern Sharing
Sparse attention methods exploit the inherent sparsity in attention to speed up the prefilling phase of long-context inference, mitigating the quadratic complexity of full attention computation.
Scaling Laws for Speculative Decoding
The escalating demand for efficient decoding in large language models (LLMs) is particularly critical for reasoning-intensive architectures like OpenAI-o3 and DeepSeek-R1, which depend on extended chain-of-thought reasoning.
DNN Training Acceleration via Exploring GPGPU Friendly Sparsity
The training phases of Deep neural network~(DNN) consumes enormous processing time and energy.
CP-ViT: Cascade Vision Transformer Pruning via Progressive Sparsity Prediction
We explore the sparsity in ViT and observe that informative patches and heads are sufficient for accurate image recognition.
Invocation-driven Neural Approximate Computing with a Multiclass-Classifier and Multiple Approximators
We thus propose a novel approximate computing architecture with a Multiclass-Classifier and Multiple Approximators (MCMA).
Approximate Random Dropout
In this paper, we propose the Approximate Random Dropout that replaces the conventional random dropout of neurons and synapses with a regular and predefined patterns to eliminate the unnecessary computation and data access.
