39 citations · 96 across the 11 of their papers we have counts for
13 papers
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…
QADAM: Quantization-Aware DNN Accelerator Modeling for Pareto-Optimality
Ahmet Inci, Siri Garudanagiri Virupaksha, Aman Jain +3
As the machine learning and systems communities strive to achieve higher energy-efficiency through custom deep neural network (DNN) accelerators, varied bit precision or quantizati…
QAPPA: Quantization-Aware Power, Performance, and Area Modeling of DNN Accelerators
Ahmet Inci, Siri Garudanagiri Virupaksha, Aman Jain +3
As the machine learning and systems community strives to achieve higher energy-efficiency through custom DNN accelerators and model compression techniques, there is a need for a de…
Single-Path Mobile AutoML: Efficient ConvNet Design and NAS Hyperparameter Optimization
Dimitrios Stamoulis, Ruizhou Ding, Di Wang +4
Can we reduce the search cost of Neural Architecture Search (NAS) from days down to only few hours? NAS methods automate the design of Convolutional Networks (ConvNets) under hardw…
ViP: Virtual Pooling for Accelerating CNN-based Image Classification and Object Detection
Zhuo Chen, Jiyuan Zhang, Ruizhou Ding +1
In recent years, Convolutional Neural Networks (CNNs) have shown superior capability in visual learning tasks. While accuracy-wise CNNs provide unprecedented performance, they are…
Single-Path NAS: Device-Aware Efficient ConvNet Design
Dimitrios Stamoulis, Ruizhou Ding, Di Wang +4
Can we automatically design a Convolutional Network (ConvNet) with the highest image classification accuracy under the latency constraint of a mobile device? Neural Architecture Se…
Single-Path NAS: Designing Hardware-Efficient ConvNets in less than 4 Hours
Dimitrios Stamoulis, Ruizhou Ding, Di Wang +4
Can we automatically design a Convolutional Network (ConvNet) with the highest image classification accuracy under the runtime constraint of a mobile device? Neural architecture se…
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…
LightNN: Filling the Gap between Conventional Deep Neural Networks and Binarized Networks
Ruizhou Ding, Zeye Liu, Rongye Shi +2
Application-specific integrated circuit (ASIC) implementations for Deep Neural Networks (DNNs) have been adopted in many systems because of their higher classification speed. Howev…