60 citations · 185 across the 13 of their papers we have counts for
23 papers
Efficient Multi-Prize Lottery Tickets: Enhanced Accuracy, Training, and Inference Speed
Hao Cheng, Pu Zhao, Yize Li +4
Recently, Diffenderfer and Kailkhura proposed a new paradigm for learning compact yet highly accurate binary neural networks simply by pruning and quantizing randomly weighted full…
ILMPQ : An Intra-Layer Multi-Precision Deep Neural Network Quantization framework for FPGA
Sung-En Chang, Yanyu Li, Mengshu Sun +2
This work targets the commonly used FPGA (field-programmable gate array) devices as the hardware platform for DNN edge computing. We focus on DNN quantization as the main model com…
RMSMP: A Novel Deep Neural Network Quantization Framework with Row-wise Mixed Schemes and Multiple Precisions
Sung-En Chang, Yanyu Li, Mengshu Sun +4
This work proposes a novel Deep Neural Network (DNN) quantization framework, namely RMSMP, with a Row-wise Mixed-Scheme and Multi-Precision approach. Specifically, this is the firs…
MEST: Accurate and Fast Memory-Economic Sparse Training Framework on the Edge
Geng Yuan, Xiaolong Ma, Wei Niu +13
Recently, a new trend of exploring sparsity for accelerating neural network training has emerged, embracing the paradigm of training on the edge. This paper proposes a novel Memory…
GRIM: A General, Real-Time Deep Learning Inference Framework for Mobile Devices based on Fine-Grained Structured Weight Sparsity
Wei Niu, Zhengang Li, Xiaolong Ma +6
It is appealing but challenging to achieve real-time deep neural network (DNN) inference on mobile devices because even the powerful modern mobile devices are considered as ``resou…
Achieving Real-Time Object Detection on MobileDevices with Neural Pruning Search
Pu Zhao, Wei Niu, Geng Yuan +4
Object detection plays an important role in self-driving cars for security development. However, mobile systems on self-driving cars with limited computation resources lead to diff…