17 citations · 47 across the 6 of their papers we have counts for
6 papers
A Classical Architecture For Digital Quantum Computers
Fang Zhang, Xing Zhu, Rui Chao +15
Scaling bottlenecks the making of digital quantum computers, posing challenges from both the quantum and the classical components. We present a classical architecture to cope with…
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…
Regularized Training and Tight Certification for Randomized Smoothed Classifier with Provable Robustness
Huijie Feng, Chunpeng Wu, Guoyang Chen +2
Recently smoothing deep neural network based classifiers via isotropic Gaussian perturbation is shown to be an effective and scalable way to provide state-of-the-art probabilistic…
Sionnx: Automatic Unit Test Generator for ONNX Conformance
Xinli Cai, Peng Zhou, Shuhan Ding +2
Open Neural Network Exchange (ONNX) is an open format to represent AI models and is supported by many machine learning frameworks. While ONNX defines unified and portable computati…
Software-defined Design Space Exploration for an Efficient DNN Accelerator Architecture
Ye Yu, Yingmin Li, Shuai Che +2
Deep neural networks (DNNs) have been shown to outperform conventional machine learning algorithms across a wide range of applications, e.g., image recognition, object detection, r…
Hardware-Guided Symbiotic Training for Compact, Accurate, yet Execution-Efficient LSTM
Hongxu Yin, Guoyang Chen, Yingmin Li +3
Many long short-term memory (LSTM) applications need fast yet compact models. Neural network compression approaches, such as the grow-and-prune paradigm, have proved to be promisin…