4 papers
FlashT2S: GPU-Accelerated Tensor-to-Spike Conversion Operators for Heterogeneous Neuromorphic LLM Backends
Rongyuan Guo, Haotian Wang, Yufeng Zhang +2
Artificial intelligence faces two growing challenges: computational bottlenecks and energy inefficiency inherent to the von Neumann architecture. Neuromorphic computing offers a pr…
ECGDeDRDNet: A deep learning-based method for Electrocardiogram noise removal using a double recurrent dense network
Sainan xiao, Wangdong Yang, Buwen Cao +1
Electrocardiogram (ECG) signals are frequently corrupted by noise, such as baseline wander (BW), muscle artifacts (MA), and electrode motion (EM), which significantly degrade their…
cuFastTuckerPlus: A Stochastic Parallel Sparse FastTucker Decomposition Using GPU Tensor Cores
Zixuan Li, Mingxing Duan, Huizhang Luo +3
Sparse tensors are prevalent in real-world applications, often characterized by their large-scale, high-order, and high-dimensional nature. Directly handling raw tensors is impract…
cuFasterTucker: A Stochastic Optimization Strategy for Parallel Sparse FastTucker Decomposition on GPU Platform
Zixuan Li
Currently, the size of scientific data is growing at an unprecedented rate. Data in the form of tensors exhibit high-order, high-dimensional, and highly sparse features. Although t…