21 citations · 51 across the 9 of their papers we have counts for
9 papers
AGS: Accelerating 3D Gaussian Splatting SLAM via CODEC-Assisted Frame Covisibility Detection
Houshu He, Naifeng Jing, Li Jiang +2
Simultaneous Localization and Mapping (SLAM) is a critical task that enables autonomous vehicles to construct maps and localize themselves in unknown environments. Recent breakthro…
DNN Training Acceleration via Exploring GPGPU Friendly Sparsity
Zhuoran Song, Yihong Xu, Han Li +3
The training phases of Deep neural network~(DNN) consumes enormous processing time and energy. Compression techniques utilizing the sparsity of DNNs can effectively accelerate the…
CP-ViT: Cascade Vision Transformer Pruning via Progressive Sparsity Prediction
Zhuoran Song, Yihong Xu, Zhezhi He +3
Vision transformer (ViT) has achieved competitive accuracy on a variety of computer vision applications, but its computational cost impedes the deployment on resource-limited mobil…
N3H-Core: Neuron-designed Neural Network Accelerator via FPGA-based Heterogeneous Computing Cores
Yu Gong, Zhihan Xu, Zhezhi He +4
Accelerating the neural network inference by FPGA has emerged as a popular option, since the reconfigurability and high performance computing capability of FPGA intrinsically satis…
SME: ReRAM-based Sparse-Multiplication-Engine to Squeeze-Out Bit Sparsity of Neural Network
Fangxin Liu, Wenbo Zhao, Yilong Zhao +6
Resistive Random-Access-Memory (ReRAM) crossbar is a promising technique for deep neural network (DNN) accelerators, thanks to its in-memory and in-situ analog computing abilities…
An Ultra-Efficient Memristor-Based DNN Framework with Structured Weight Pruning and Quantization Using ADMM
Geng Yuan, Xiaolong Ma, Caiwen Ding +7
The high computation and memory storage of large deep neural networks (DNNs) models pose intensive challenges to the conventional Von-Neumann architecture, incurring substantial da…
Invocation-driven Neural Approximate Computing with a Multiclass-Classifier and Multiple Approximators
Haiyue Song, Chengwen Xu, Qiang Xu +4
Neural approximate computing gains enormous energy-efficiency at the cost of tolerable quality-loss. A neural approximator can map the input data to output while a classifier deter…
AXNet: ApproXimate computing using an end-to-end trainable neural network
Zhenghao Peng, Xuyang Chen, Chengwen Xu +4
Neural network based approximate computing is a universal architecture promising to gain tremendous energy-efficiency for many error resilient applications. To guarantee the approx…
Approximate Random Dropout
Zhuoran Song, Ru Wang, Dongyu Ru +5
The training phases of Deep neural network~(DNN) consumes enormous processing time and energy. Compression techniques utilizing the sparsity of DNNs can effectively accelerate the…