6.5k citations · 18.4k across the 52 of their papers we have counts for
12 papers · 1 filter
AET-SGD: Asynchronous Event-triggered Stochastic Gradient Descent
Nhuong Nguyen, Song Han
Communication cost is the main bottleneck for the design of effective distributed learning algorithms. Recently, event-triggered techniques have been proposed to reduce the exchang…
NAAS: Neural Accelerator Architecture Search
Yujun Lin, Mengtian Yang, Song Han
Data-driven, automatic design space exploration of neural accelerator architecture is desirable for specialization and productivity. Previous frameworks focus on sizing the numeric…
IOS: Inter-Operator Scheduler for CNN Acceleration
Yaoyao Ding, Ligeng Zhu, Zhihao Jia +2
To accelerate CNN inference, existing deep learning frameworks focus on optimizing intra-operator parallelization. However, a single operator can no longer fully utilize the availa…
APQ: Joint Search for Network Architecture, Pruning and Quantization Policy
Tianzhe Wang, Kuan Wang, Han Cai +3
We present APQ for efficient deep learning inference on resource-constrained hardware. Unlike previous methods that separately search the neural architecture, pruning policy, and q…
Once-for-All: Train One Network and Specialize it for Efficient Deployment
Han Cai, Chuang Gan, Tianzhe Wang +2
We address the challenging problem of efficient inference across many devices and resource constraints, especially on edge devices. Conventional approaches either manually design o…
Deep Leakage from Gradients
Ligeng Zhu, Zhijian Liu, Song Han
Exchanging gradients is a widely used method in modern multi-node machine learning system (e.g., distributed training, collaborative learning). For a long time, people believed tha…
Design Automation for Efficient Deep Learning Computing
Song Han, Han Cai, Ligeng Zhu +4
Efficient deep learning computing requires algorithm and hardware co-design to enable specialization: we usually need to change the algorithm to reduce memory footprint and improve…
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…
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…
Exploring the Regularity of Sparse Structure in Convolutional Neural Networks
Huizi Mao, Song Han, Jeff Pool +4
Sparsity helps reduce the computational complexity of deep neural networks by skipping zeros. Taking advantage of sparsity is listed as a high priority in next generation DNN accel…
Trained Ternary Quantization
Chenzhuo Zhu, Song Han, Huizi Mao +1
Deep neural networks are widely used in machine learning applications. However, the deployment of large neural networks models can be difficult to deploy on mobile devices with lim…