
Towards an Efficient and General Framework of Robust Training for Graph Neural Networks
Graph Neural Networks (GNNs) have made significant advances on several f...
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Tiny but Accurate: A Pruned, Quantized and Optimized Memristor Crossbar Framework for Ultra Efficient DNN Implementation
The stateofart DNN structures involve intensive computation and high m...
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A Unified DNN Weight Compression Framework Using Reweighted Optimization Methods
To address the large model size and intensive computation requirement of...
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An UltraEfficient MemristorBased DNN Framework with Structured Weight Pruning and Quantization Using ADMM
The high computation and memory storage of large deep neural networks (D...
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CirCNN: Accelerating and Compressing Deep Neural Networks Using BlockCirculantWeight Matrices
Largescale deep neural networks (DNNs) are both compute and memory inte...
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HardwareDriven Nonlinear Activation for Stochastic Computing Based Deep Convolutional Neural Networks
Recently, Deep Convolutional Neural Networks (DCNNs) have made unprecede...
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SCDCNN: HighlyScalable Deep Convolutional Neural Network using Stochastic Computing
With recent advancing of Internet of Things (IoTs), it becomes very attr...
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FFTBased Deep Learning Deployment in Embedded Systems
Deep learning has delivered its powerfulness in many application domains...
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Towards UltraHigh Performance and Energy Efficiency of Deep Learning Systems: An AlgorithmHardware CoOptimization Framework
Hardware accelerations of deep learning systems have been extensively in...
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Efficient Recurrent Neural Networks using Structured Matrices in FPGAs
Recurrent Neural Networks (RNNs) are becoming increasingly important for...
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Learning Topics using Semantic Locality
The topic modeling discovers the latent topic probability of the given t...
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Towards BudgetDriven Hardware Optimization for Deep Convolutional Neural Networks using Stochastic Computing
Recently, Deep Convolutional Neural Network (DCNN) has achieved tremendo...
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ERNN: Design Optimization for Efficient Recurrent Neural Networks in FPGAs
Recurrent Neural Networks (RNNs) are becoming increasingly important for...
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VIBNN: Hardware Acceleration of Bayesian Neural Networks
Bayesian Neural Networks (BNNs) have been proposed to address the proble...
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CLSTM: Enabling Efficient LSTM using Structured Compression Techniques on FPGAs
Recently, significant accuracy improvement has been achieved for acousti...
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Structured Weight MatricesBased Hardware Accelerators in Deep Neural Networks: FPGAs and ASICs
Both industry and academia have extensively investigated hardware accele...
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A StochasticComputing based Deep Learning Framework using Adiabatic QuantumFluxParametron SuperconductingTechnology
The Adiabatic QuantumFluxParametron (AQFP) superconducting technology ...
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Deep Compressed Pneumonia Detection for LowPower Embedded Devices
Deep neural networks (DNNs) have been expanded into medical fields and t...
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A SOTMRAMbased ProcessingInMemory Engine for Highly Compressed DNN Implementation
The computing wall and data movement challenges of deep neural networks ...
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REQYOLO: A ResourceAware, Efficient Quantization Framework for Object Detection on FPGAs
Deep neural networks (DNNs), as the basis of object detection, will play...
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PredictionBased Fast Thermoelectric Generator Reconfiguration for Energy Harvesting from Vehicle Radiators
Thermoelectric generation (TEG) has increasingly drawn attention for bei...
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Caiwen Ding
verfied profile
Assistant professor at the University of Connecticut