6.5k citations · 18.4k across the 52 of their papers we have counts for
8 papers · 1 filter
Learning to Design Circuits
Hanrui Wang, Jiacheng Yang, Hae-Seung Lee +1
Analog IC design relies on human experts to search for parameters that satisfy circuit specifications with their experience and intuitions, which is highly labor intensive, time co…
ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware
Han Cai, Ligeng Zhu, Song Han
Neural architecture search (NAS) has a great impact by automatically designing effective neural network architectures. However, the prohibitive computational demand of conventional…
HAQ: Hardware-Aware Automated Quantization with Mixed Precision
Kuan Wang, Zhijian Liu, Yujun Lin +2
Model quantization is a widely used technique to compress and accelerate deep neural network (DNN) inference. Emergent DNN hardware accelerators begin to support mixed precision (1…
TSM: Temporal Shift Module for Efficient Video Understanding
Ji Lin, Chuang Gan, Song Han
The explosive growth in video streaming gives rise to challenges on performing video understanding at high accuracy and low computation cost. Conventional 2D CNNs are computational…
Path-Level Network Transformation for Efficient Architecture Search
Han Cai, Jiacheng Yang, Weinan Zhang +2
We introduce a new function-preserving transformation for efficient neural architecture search. This network transformation allows reusing previously trained networks and existing…
Fast inference of deep neural networks in FPGAs for particle physics
Javier Duarte, Song Han, Philip Harris +8
Recent results at the Large Hadron Collider (LHC) have pointed to enhanced physics capabilities through the improvement of the real-time event processing techniques. Machine learni…
Efficient Sparse-Winograd Convolutional Neural Networks
Xingyu Liu, Jeff Pool, Song Han +1
Convolutional Neural Networks (CNNs) are computationally intensive, which limits their application on mobile devices. Their energy is dominated by the number of multiplies needed t…
AMC: AutoML for Model Compression and Acceleration on Mobile Devices
Yihui He, Ji Lin, Zhijian Liu +3
Model compression is a critical technique to efficiently deploy neural network models on mobile devices which have limited computation resources and tight power budgets. Convention…