
HardwareCentric AutoML for MixedPrecision Quantization
Model quantization is a widely used technique to compress and accelerate...
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Searching Efficient 3D Architectures with Sparse PointVoxel Convolution
Selfdriving cars need to understand 3D scenes efficiently and accuratel...
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Tiny Transfer Learning: Towards MemoryEfficient OnDevice Learning
We present TinyTransferLearning (TinyTL), an efficient ondevice learn...
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MCUNet: Tiny Deep Learning on IoT Devices
Machine learning on tiny IoT devices based on microcontroller units (MCU...
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Differentiable Augmentation for DataEfficient GAN Training
The performance of generative adversarial networks (GANs) heavily deteri...
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APQ: Joint Search for Network Architecture, Pruning and Quantization Policy
We present APQ for efficient deep learning inference on resourceconstra...
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HAT: HardwareAware Transformers for Efficient Natural Language Processing
Transformers are ubiquitous in Natural Language Processing (NLP) tasks, ...
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MicroNet for Efficient Language Modeling
It is important to design compact language models for efficient deployme...
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GCNRL Circuit Designer: Transferable Transistor Sizing with Graph Neural Networks and Reinforcement Learning
Automatic transistor sizing is a challenging problem in circuit design d...
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Lite Transformer with LongShort Range Attention
Transformer has become ubiquitous in natural language processing (e.g., ...
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A Fast Algorithm for SourceWise RoundTrip Spanners
In this paper, we study the problem of efficiently constructing sourcew...
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GAN Compression: Efficient Architectures for Interactive Conditional GANs
Conditional Generative Adversarial Networks (cGANs) have enabled control...
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SpArch: Efficient Architecture for Sparse Matrix Multiplication
Generalized Sparse MatrixMatrix Multiplication (SpGEMM) is a ubiquitous...
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ChainSplitter: Towards Blockchainbased Industrial IoT Architecture for Supporting Hierarchical Storage
The fast developing Industrial Internet of Things (IIoT) technologies pr...
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Training Kinetics in 15 Minutes: Largescale Distributed Training on Videos
Deep video recognition is more computationally expensive than image reco...
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Once for All: Train One Network and Specialize it for Efficient Deployment
Efficient deployment of deep learning models requires specialized neural...
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PointVoxel CNN for Efficient 3D Deep Learning
We present PointVoxel CNN (PVCNN) for efficient, fast 3D deep learning....
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Deep Leakage from Gradients
Exchanging gradients is a widely used method in modern multinode machin...
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Design Automation for Efficient Deep Learning Computing
Efficient deep learning computing requires algorithm and hardware codes...
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Defensive Quantization: When Efficiency Meets Robustness
Neural network quantization is becoming an industry standard to efficien...
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SysML: The New Frontier of Machine Learning Systems
Machine learning (ML) techniques are enjoying rapidly increasing adoptio...
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Fully Distributed Packet Scheduling Framework for Handling Disturbances in Lossy RealTime Wireless Networks
Along with the rapid growth of Industrial InternetofThings (IIoT) appl...
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Learning to Design Circuits
Analog IC design relies on human experts to search for parameters that s...
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ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware
Neural architecture search (NAS) has a great impact by automatically des...
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HAQ: HardwareAware Automated Quantization
Model quantization is a widely used technique to compress and accelerate...
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Temporal Shift Module for Efficient Video Understanding
The explosive growth in online video streaming gives rise to challenges ...
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CommunicationOptimal Distributed Dynamic Graph Clustering
We consider the problem of clustering graph nodes over largescale dynam...
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PathLevel Network Transformation for Efficient Architecture Search
We introduce a new functionpreserving transformation for efficient neur...
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Fast inference of deep neural networks in FPGAs for particle physics
Recent results at the Large Hadron Collider (LHC) have pointed to enhanc...
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RTDAP: A RealTime Data Analytics Platform for Largescale Industrial Process Monitoring and Control
In most process control systems nowadays, process measurements are perio...
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Efficient SparseWinograd Convolutional Neural Networks
Convolutional Neural Networks (CNNs) are computationally intensive, whic...
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ADC: Automated Deep Compression and Acceleration with Reinforcement Learning
Model compression is an effective technique facilitating the deployment ...
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Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training
Largescale distributed training requires significant communication band...
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Deep Generative Adversarial Networks for Compressed Sensing Automates MRI
Magnetic resonance image (MRI) reconstruction is a severely illposed li...
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Exploring the Regularity of Sparse Structure in Convolutional Neural Networks
Sparsity helps reduce the computational complexity of deep neural networ...
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Classification of Neurological Gait Disorders Using Multitask Feature Learning
As our population ages, neurological impairments and degeneration of the...
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ESE: Efficient Speech Recognition Engine with Sparse LSTM on FPGA
Long ShortTerm Memory (LSTM) is widely used in speech recognition. In o...
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DSD: DenseSparseDense Training for Deep Neural Networks
Modern deep neural networks have a large number of parameters, making th...
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SqueezeNet: AlexNetlevel accuracy with 50x fewer parameters and <0.5MB model size
Recent research on deep neural networks has focused primarily on improvi...
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Generate Image Descriptions based on Deep RNN and Memory Cells for Images Features
Generating natural language descriptions for images is a challenging tas...
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EIE: Efficient Inference Engine on Compressed Deep Neural Network
Stateoftheart deep neural networks (DNNs) have hundreds of millions o...
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Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding
Neural networks are both computationally intensive and memory intensive,...
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Learning both Weights and Connections for Efficient Neural Networks
Neural networks are both computationally intensive and memory intensive,...
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Robust Face Recognition using Local Illumination Normalization and Discriminant Feature Point Selection
Face recognition systems must be robust to the variation of various fact...
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Song Han
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Assistant professor in the Electrical Engineering and Computer Science Department of the Massachusetts Institute of Technology (MIT).