7 citations · 23 across the 9 of their papers we have counts for
9 papers
MEGA: A Memory-Efficient GNN Accelerator Exploiting Degree-Aware Mixed-Precision Quantization
Zeyu Zhu, Fanrong Li, Gang Li +5
Graph Neural Networks (GNNs) are becoming a promising technique in various domains due to their excellent capabilities in modeling non-Euclidean data. Although a spectrum of accele…
: Aggregation-Aware Quantization for Graph Neural Networks
Zeyu Zhu, Fanrong Li, Zitao Mo +5
As graph data size increases, the vast latency and memory consumption during inference pose a significant challenge to the real-world deployment of Graph Neural Networks (GNNs). Wh…
Optimal plug-in Gaussian processes for modeling derivatives
Zejian Liu, Meng Li
Derivatives are a key nonparametric functional in wide-ranging applications where the rate of change of an unknown function is of interest. In the Bayesian paradigm, Gaussian proce…
Block Convolution: Towards Memory-Efficient Inference of Large-Scale CNNs on FPGA
Gang Li, Zejian Liu, Fanrong Li +1
Deep convolutional neural networks have achieved remarkable progress in recent years. However, the large volume of intermediate results generated during inference poses a significa…
Hardware Acceleration of Fully Quantized BERT for Efficient Natural Language Processing
Zejian Liu, Gang Li, Jian Cheng
BERT is the most recent Transformer-based model that achieves state-of-the-art performance in various NLP tasks. In this paper, we investigate the hardware acceleration of BERT on…
Semiparametric Bayesian Inference for Local Extrema of Functions in the Presence of Noise
Meng Li, Zejian Liu, Cheng-Han Yu +1
There is a wide range of applications where the local extrema of a function are the key quantity of interest. However, there is surprisingly little work on methods to infer local e…
Equivalence of Convergence Rates of Posterior Distributions and Bayes Estimators for Functions and Nonparametric Functionals
Zejian Liu, Meng Li
We study the posterior contraction rates of a Bayesian method with Gaussian process priors in nonparametric regression and its plug-in property for differential operators. For a ge…
On the Estimation of Derivatives Using Plug-in Kernel Ridge Regression Estimators
Zejian Liu, Meng Li
We study the problem of estimating the derivatives of a regression function, which has a wide range of applications as a key nonparametric functional of unknown functions. Standard…
A System-Level Solution for Low-Power Object Detection
Fanrong Li, Zitao Mo, Peisong Wang +8
Object detection has made impressive progress in recent years with the help of deep learning. However, state-of-the-art algorithms are both computation and memory intensive. Though…