79 citations · 383 across the 28 of their papers we have counts for
6 papers · 1 filter
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…