39 citations · 123 across the 13 of their papers we have counts for
14 papers
Play It Cool: Dynamic Shifting Prevents Thermal Throttling
Yang Zhou, Feng Liang, Ting-wu Chin +1
Machine learning (ML) has entered the mobile era where an enormous number of ML models are deployed on edge devices. However, running common ML models on edge devices continuously…
QUIDAM: A Framework for Quantization-Aware DNN Accelerator and Model Co-Exploration
Ahmet Inci, Siri Garudanagiri Virupaksha, Aman Jain +4
As the machine learning and systems communities strive to achieve higher energy-efficiency through custom deep neural network (DNN) accelerators, varied precision or quantization l…
Width Transfer: On the (In)variance of Width Optimization
Ting-Wu Chin, Diana Marculescu, Ari S. Morcos
Optimizing the channel counts for different layers of a CNN has shown great promise in improving the efficiency of CNNs at test-time. However, these methods often introduce large c…
One Weight Bitwidth to Rule Them All
Ting-Wu Chin, Pierce I-Jen Chuang, Vikas Chandra +1
Weight quantization for deep ConvNets has shown promising results for applications such as image classification and semantic segmentation and is especially important for applicatio…
Joslim: Joint Widths and Weights Optimization for Slimmable Neural Networks
Ting-Wu Chin, Ari S. Morcos, Diana Marculescu
Slimmable neural networks provide a flexible trade-off front between prediction error and computational requirement (such as the number of floating-point operations or FLOPs) with…
Renofeation: A Simple Transfer Learning Method for Improved Adversarial Robustness
Ting-Wu Chin, Cha Zhang, Diana Marculescu
Fine-tuning through knowledge transfer from a pre-trained model on a large-scale dataset is a widely spread approach to effectively build models on small-scale datasets. In this wo…
FLightNNs: Lightweight Quantized Deep Neural Networks for Fast and Accurate Inference
Ruizhou Ding, Zeye Liu, Ting-Wu Chin +3
To improve the throughput and energy efficiency of Deep Neural Networks (DNNs) on customized hardware, lightweight neural networks constrain the weights of DNNs to be a limited com…
Regularizing Activation Distribution for Training Binarized Deep Networks
Ruizhou Ding, Ting-Wu Chin, Zeye Liu +1
Binarized Neural Networks (BNNs) can significantly reduce the inference latency and energy consumption in resource-constrained devices due to their pure-logical computation and few…
Towards Efficient Model Compression via Learned Global Ranking
Ting-Wu Chin, Ruizhou Ding, Cha Zhang +1
Pruning convolutional filters has demonstrated its effectiveness in compressing ConvNets. Prior art in filter pruning requires users to specify a target model complexity (e.g., mod…
AdaScale: Towards Real-time Video Object Detection Using Adaptive Scaling
Ting-Wu Chin, Ruizhou Ding, Diana Marculescu
In vision-enabled autonomous systems such as robots and autonomous cars, video object detection plays a crucial role, and both its speed and accuracy are important factors to provi…
Understanding the Impact of Label Granularity on CNN-based Image Classification
Zhuo Chen, Ruizhou Ding, Ting-Wu Chin +1
In recent years, supervised learning using Convolutional Neural Networks (CNNs) has achieved great success in image classification tasks, and large scale labeled datasets have cont…
Layer-compensated Pruning for Resource-constrained Convolutional Neural Networks
Ting-Wu Chin, Cha Zhang, Diana Marculescu
Resource-efficient convolution neural networks enable not only the intelligence on edge devices but also opportunities in system-level optimization such as scheduling. In this work…
Domain Specific Approximation for Object Detection
Ting-Wu Chin, Chia-Lin Yu, Matthew Halpern +3
There is growing interest in object detection in advanced driver assistance systems and autonomous robots and vehicles. To enable such innovative systems, we need faster object det…
Designing Adaptive Neural Networks for Energy-Constrained Image Classification
Dimitrios Stamoulis, Ting-Wu Chin, Anand Krishnan Prakash +4
As convolutional neural networks (CNNs) enable state-of-the-art computer vision applications, their high energy consumption has emerged as a key impediment to their deployment on e…