6.5k citations · 10.4k across the 118 of their papers we have counts for
6 papers · 1 filter
SqueezeDet: Unified, Small, Low Power Fully Convolutional Neural Networks for Real-Time Object Detection for Autonomous Driving
Bichen Wu, Alvin Wan, Forrest Iandola +2
Object detection is a crucial task for autonomous driving. In addition to requiring high accuracy to ensure safety, object detection for autonomous driving also requires real-time…
A Metaprogramming and Autotuning Framework for Deploying Deep Learning Applications
Matthew W. Moskewicz, Ali Jannesari, Kurt Keutzer
In recent years, deep neural networks (DNNs), have yielded strong results on a wide range of applications. Graphics Processing Units (GPUs) have been one key enabling factor leadin…
How to scale distributed deep learning?
Peter H. Jin, Qiaochu Yuan, Forrest Iandola +1
Training time on large datasets for deep neural networks is the principal workflow bottleneck in a number of important applications of deep learning, such as object classification…
Shallow Networks for High-Accuracy Road Object-Detection
Khalid Ashraf, Bichen Wu, Forrest N. Iandola +2
The ability to automatically detect other vehicles on the road is vital to the safety of partially-autonomous and fully-autonomous vehicles. Most of the high-accuracy techniques fo…
Boda-RTC: Productive Generation of Portable, Efficient Code for Convolutional Neural Networks on Mobile Computing Platforms
Matthew Moskewicz, Forrest Iandola, Kurt Keutzer
The popularity of neural networks (NNs) spans academia, industry, and popular culture. In particular, convolutional neural networks (CNNs) have been applied to many image based mac…
SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size
Forrest N. Iandola, Song Han, Matthew W. Moskewicz +3
Recent research on deep neural networks has focused primarily on improving accuracy. For a given accuracy level, it is typically possible to identify multiple DNN architectures tha…