Song Han
#151,352
Most Influential Person Now
Song Han's AcademicInfluence.com Rankings
Song Hanengineering Degrees
Engineering
#6126
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#7431
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Electrical Engineering
#1783
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Song Hancomputer-science Degrees
Computer Science
#7989
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#8406
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Algorithms
#315
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#319
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Machine Learning
#3143
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#3182
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Artificial Intelligence
#3444
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#3494
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Engineering Computer Science
Song Han's Degrees
- PhD Electrical Engineering and Computer Science Stanford University
- Masters Electrical Engineering Stanford University
- Bachelors Electrical Engineering Tsinghua University
Why Is Song Han Influential?
(Suggest an Edit or Addition)Song Han's Published Works
Number of citations in a given year to any of this author's works
Total number of citations to an author for the works they published in a given year. This highlights publication of the most important work(s) by the author
Published Works
- Deep Compression: Compressing Deep Neural Network with Pruning, Trained Quantization and Huffman Coding (2015) (6725)
- SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <1MB model size (2016) (5225)
- Learning both Weights and Connections for Efficient Neural Network (2015) (4910)
- EIE: Efficient Inference Engine on Compressed Deep Neural Network (2016) (2092)
- ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware (2018) (1397)
- AMC: AutoML for Model Compression and Acceleration on Mobile Devices (2018) (1046)
- Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training (2017) (980)
- TSM: Temporal Shift Module for Efficient Video Understanding (2018) (966)
- Deep Leakage from Gradients (2019) (938)
- Trained Ternary Quantization (2016) (914)
- Once for All: Train One Network and Specialize it for Efficient Deployment (2019) (755)
- - LEVEL ACCURACY WITH 50 X FEWER PARAMETERS AND < 0 . 5 MB MODEL SIZE (2016) (672)
- HAQ: Hardware-Aware Automated Quantization With Mixed Precision (2018) (563)
- ESE: Efficient Speech Recognition Engine with Sparse LSTM on FPGA (2016) (546)
- Model Compression and Hardware Acceleration for Neural Networks: A Comprehensive Survey (2020) (352)
- Point-Voxel CNN for Efficient 3D Deep Learning (2019) (346)
- Angel-Eye: A Complete Design Flow for Mapping CNN Onto Embedded FPGA (2018) (343)
- Differentiable Augmentation for Data-Efficient GAN Training (2020) (321)
- Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution (2020) (262)
- Fast inference of deep neural networks in FPGAs for particle physics (2018) (243)
- Exploring the Regularity of Sparse Structure in Convolutional Neural Networks (2017) (211)
- Path-Level Network Transformation for Efficient Architecture Search (2018) (184)
- MCUNet: Tiny Deep Learning on IoT Devices (2020) (174)
- Temporal Shift Module for Efficient Video Understanding (2018) (168)
- Lite Transformer with Long-Short Range Attention (2020) (165)
- DSD: Dense-Sparse-Dense Training for Deep Neural Networks (2016) (155)
- Deep Generative Adversarial Networks for Compressed Sensing Automates MRI (2017) (144)
- HAT: Hardware-Aware Transformers for Efficient Natural Language Processing (2020) (140)
- GAN Compression: Efficient Architectures for Interactive Conditional GANs (2020) (125)
- SpArch: Efficient Architecture for Sparse Matrix Multiplication (2020) (121)
- Domain-specific hardware accelerators (2020) (116)
- APQ: Joint Search for Network Architecture, Pruning and Quantization Policy (2020) (110)
- Exploring the Granularity of Sparsity in Convolutional Neural Networks (2017) (106)
- SpAtten: Efficient Sparse Attention Architecture with Cascade Token and Head Pruning (2020) (104)
- Efficient Sparse-Winograd Convolutional Neural Networks (2018) (103)
- GCN-RL Circuit Designer: Transferable Transistor Sizing with Graph Neural Networks and Reinforcement Learning (2020) (99)
- TinyTL: Reduce Memory, Not Parameters for Efficient On-Device Learning (2020) (94)
- DSD: Regularizing Deep Neural Networks with Dense-Sparse-Dense Training Flow (2016) (77)
- HAQ: Hardware-Aware Automated Quantization (2018) (75)
- ADC: Automated Deep Compression and Acceleration with Reinforcement Learning (2018) (70)
- Software-Hardware Codesign for Efficient Neural Network Acceleration (2017) (62)
- A Deep Neural Network Compression Pipeline: Pruning, Quantization, Huffman Encoding (2015) (58)
- Learning to Design Circuits (2018) (56)
- A Configurable Multi-Precision CNN Computing Framework Based on Single Bit RRAM (2019) (54)
- Anycost GANs for Interactive Image Synthesis and Editing (2021) (50)
- QuantumNAS: Noise-Adaptive Search for Robust Quantum Circuits (2021) (50)
- Angel-Eye: A Complete Design Flow for Mapping CNN onto Customized Hardware (2016) (50)
- Deep compression and EIE: Efficient inference engine on compressed deep neural network (2016) (43)
- ESE: Efficient Speech Recognition Engine with Compressed LSTM on FPGA (2016) (41)
- From model to FPGA: Software-hardware co-design for efficient neural network acceleration (2016) (40)
- IOS: Inter-Operator Scheduler for CNN Acceleration (2020) (35)
- MCUNetV2: Memory-Efficient Patch-based Inference for Tiny Deep Learning (2021) (33)
- Long Live TIME: Improving Lifetime for Training-In-Memory Engines by Structured Gradient Sparsification (2018) (32)
- AutoML for Architecting Efficient and Specialized Neural Networks (2020) (24)
- Adaptive Mixture of Low-Rank Factorizations for Compact Neural Modeling (2018) (23)
- NAAS: Neural Accelerator Architecture Search (2021) (23)
- An FPGA Design Framework for CNN Sparsification and Acceleration (2017) (21)
- VISTA 2.0: An Open, Data-driven Simulator for Multimodal Sensing and Policy Learning for Autonomous Vehicles (2021) (20)
- DataMix: Efficient Privacy-Preserving Edge-Cloud Inference (2020) (20)
- TSM: Temporal Shift Module for Efficient and Scalable Video Understanding on Edge Devices (2020) (20)
- Tiny Transfer Learning: Towards Memory-Efficient On-Device Learning (2020) (19)
- SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models (2022) (16)
- Efficient and Robust LiDAR-Based End-to-End Navigation (2021) (14)
- PointAcc: Efficient Point Cloud Accelerator (2021) (14)
- Design Automation for Efficient Deep Learning Computing (2019) (13)
- On-Device Image Classification with Proxyless Neural Architecture Search and Quantization-Aware Fine-Tuning (2019) (12)
- PVNAS: 3D Neural Architecture Search With Point-Voxel Convolution (2021) (12)
- TinyTL: Reduce Activations, Not Trainable Parameters for Efficient On-Device Learning (2020) (11)
- Defensive Quantization: When Efficiency Meets Robustness (2019) (11)
- Training Kinetics in 15 Minutes: Large-scale Distributed Training on Videos (2019) (9)
- Hardware-Centric AutoML for Mixed-Precision Quantization (2020) (9)
- Reconfigurable processor for deep learning in autonomous vehicles (2017) (8)
- Network Augmentation for Tiny Deep Learning (2021) (8)
- RoQNN: Noise-Aware Training for Robust Quantum Neural Networks (2021) (8)
- Bandwidth-efficient deep learning (2018) (8)
- MicroNet for Efficient Language Modeling (2020) (7)
- Long Live TIME: Improving Lifetime and Security for NVM-Based Training-in-Memory Systems (2020) (7)
- On-chip QNN: Towards Efficient On-Chip Training of Quantum Neural Networks (2022) (6)
- On-Demand Dynamic Branch Prediction (2015) (6)
- HARDWARE-FRIENDLY CONVOLUTIONAL NEURAL NETWORK WITH EVEN-NUMBER FILTER SIZE (2016) (6)
- LocTex: Learning Data-Efficient Visual Representations from Localized Textual Supervision (2021) (6)
- Real-time pedestrian detection and tracking on customized hardware (2016) (6)
- SemAlign: Annotation-Free Camera-LiDAR Calibration with Semantic Alignment Loss (2021) (6)
- NeuralTalk on Embedded System and GPU-accelerated RNN (2015) (5)
- Energy Efficient On-Demand Dynamic Branch Prediction Models (2020) (4)
- A Fine-Grained Sparse Accelerator for Multi-Precision DNN (2019) (4)
- Time-domain segmentation based massively parallel simulation for ADCs (2013) (2)
- AET-SGD: Asynchronous Event-triggered Stochastic Gradient Descent (2021) (2)
- Fast Inference of Deep Neural Networks for Real-time Particle Physics Applications (2019) (2)
- PatchNet - Short-range Template Matching for Efficient Video Processing (2021) (2)
- INVITED: Bandwidth-Efficient Deep Learning (2018) (1)
- Generate Image Descriptions based on Deep RNN and Memory Cells for Images Features (2016) (1)
- Efficient Neural Network Architectures (2022) (0)
- GAN Compression: Efficient Architectures for Interactive Conditional GANs (2020) (0)
- FERMILAB-SLIDES-19-706-SCD-V (2019) (0)
- Image Captioning with Sparse LTSM (2017) (0)
- Bandwidth-Efficient Deep Learning — — from Compression to Acceleration (2018) (0)
- CIALIZE IT FOR EFFICIENT DEPLOYMENT (2020) (0)
- EMS: End-to-End Model Search for Network Architecture, Pruning and Quantization (2019) (0)
- Putting AI on Diet: TinyML and Efficient Deep Learning (2021) (0)
- DISTRIBUTED TRAINING (2018) (0)
- Lab41 Reading Group: SqueezeNet (2016) (0)
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