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20172023
most citedCirCNN: Accelerating and Compressing Deep Neural Networks Using Block-CirculantWeight Matrices

177 citations · 343 across the 33 of their papers we have counts for

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5 papers · 1 filter

cs.CV2023

AdaDiff: Accelerating Diffusion Models through Step-Wise Adaptive Computation

Shengkun Tang, Yaqing Wang, Caiwen Ding +3

Diffusion models achieve great success in generating diverse and high-fidelity images, yet their widespread application, especially in real-time scenarios, is hampered by their inh…

cs.CV201915 cited

REQ-YOLO: A Resource-Aware, Efficient Quantization Framework for Object Detection on FPGAs

Caiwen Ding, Shuo Wang, Ning Liu +3

Deep neural networks (DNNs), as the basis of object detection, will play a key role in the development of future autonomous systems with full autonomy. The autonomous systems have…

cs.CV20181 cited

E-RNN: Design Optimization for Efficient Recurrent Neural Networks in FPGAs

Zhe Li, Caiwen Ding, Siyue Wang +8

Recurrent Neural Networks (RNNs) are becoming increasingly important for time series-related applications which require efficient and real-time implementations. The two major types…

cs.CV2017177 cited

CirCNN: Accelerating and Compressing Deep Neural Networks Using Block-CirculantWeight Matrices

Caiwen Ding, Siyu Liao, Yanzhi Wang +13

Large-scale deep neural networks (DNNs) are both compute and memory intensive. As the size of DNNs continues to grow, it is critical to improve the energy efficiency and performanc…

cs.CV20172 cited

Hardware-Driven Nonlinear Activation for Stochastic Computing Based Deep Convolutional Neural Networks

Ji Li, Zihao Yuan, Zhe Li +5

Recently, Deep Convolutional Neural Networks (DCNNs) have made unprecedented progress, achieving the accuracy close to, or even better than human-level perception in various tasks.…