79 citations · 383 across the 28 of their papers we have counts for
31 papers
Efficient Low-rank Backpropagation for Vision Transformer Adaptation
Yuedong Yang, Hung-Yueh Chiang, Guihong Li +2
The increasing scale of vision transformers (ViT) has made the efficient fine-tuning of these large models for specific needs a significant challenge in various applications. This…
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…
DeepNVM++: Cross-Layer Modeling and Optimization Framework of Non-Volatile Memories for Deep Learning
Ahmet Inci, Mehmet Meric Isgenc, Diana Marculescu
Non-volatile memory (NVM) technologies such as spin-transfer torque magnetic random access memory (STT-MRAM) and spin-orbit torque magnetic random access memory (SOT-MRAM) have sig…
The Architectural Implications of Distributed Reinforcement Learning on CPU-GPU Systems
Ahmet Inci, Evgeny Bolotin, Yaosheng Fu +4
With deep reinforcement learning (RL) methods achieving results that exceed human capabilities in games, robotics, and simulated environments, continued scaling of RL training is 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…
Workshops on Extreme Scale Design Automation (ESDA) Challenges and Opportunities for 2025 and Beyond
R. Iris Bahar, Alex K. Jones, Srinivas Katkoori +3
Integrated circuits and electronic systems, as well as design technologies, are evolving at a great rate -- both quantitatively and qualitatively. Major developments include new in…
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…
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…
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…
Learning-based Application-Agnostic 3D NoC Design for Heterogeneous Manycore Systems
Biresh Kumar Joardar, Ryan Gary Kim, Janardhan Rao Doppa +3
The rising use of deep learning and other big-data algorithms has led to an increasing demand for hardware platforms that are computationally powerful, yet energy-efficient. Due to…
Hardware-Aware Machine Learning: Modeling and Optimization
Diana Marculescu, Dimitrios Stamoulis, Ermao Cai
Recent breakthroughs in Deep Learning (DL) applications have made DL models a key component in almost every modern computing system. The increased popularity of DL applications dep…
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…
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…
Tractable Learning and Inference for Large-Scale Probabilistic Boolean Networks
Ifigeneia Apostolopoulou, Diana Marculescu
Probabilistic Boolean Networks (PBNs) have been previously proposed so as to gain insights into complex dy- namical systems. However, identification of large networks and of the un…
Priority-Aware Near-Optimal Scheduling for Heterogeneous Multi-Core Systems with Specialized Accelerators
Zhuo Chen, Diana Marculescu
To deliver high performance in power limited systems, architects have turned to using heterogeneous systems, either CPU+GPU or mixed CPU-hardware systems. However, in systems with…
Task Scheduling for Heterogeneous Multicore Systems
Zhuo Chen, Diana Marculescu
In recent years, as the demand for low energy and high performance computing has steadily increased, heterogeneous computing has emerged as an important and promising solution. Bec…