no code implementations • 1 May 2024 • Mohanad Odema, Luke Chen, Hyoukjun Kwon, Mohammad Abdullah Al Faruque
Emerging multi-model workloads with heavy models like recent large language models significantly increased the compute and memory demands on hardware.
no code implementations • 22 Feb 2024 • Eugen Šlapak, Matúš Dopiriak, Mohammad Abdullah Al Faruque, Juraj Gazda, Marco Levorato
For autonomous mobility, it enables new possibilities with edge computing and digital twins (DTs) that offer virtual prototyping, prediction, and more.
no code implementations • 20 Feb 2024 • Junyao Wang, Mohammad Abdullah Al Faruque
Many real-world applications of the Internet of Things (IoT) employ machine learning (ML) algorithms to analyze time series information collected by interconnected sensors.
1 code implementation • 8 Jan 2024 • Mohamad Fakih, Rahul Dharmaji, Yasamin Moghaddas, Gustavo Quiros Araya, Oluwatosin Ogundare, Mohammad Abdullah Al Faruque
Although Large Language Models (LLMs) have established pre-dominance in automated code generation, they are not devoid of shortcomings.
no code implementations • 14 Dec 2023 • Mohanad Odema, Hyoukjun Kwon, Mohammad Abdullah Al Faruque
To address increasing compute demand from recent multi-model workloads with heavy models like large language models, we propose to deploy heterogeneous chiplet-based multi-chip module (MCM)-based accelerators.
no code implementations • 13 Nov 2023 • Junyao Wang, Mohammad Abdullah Al Faruque
In this work, we present dynamic HDC learning frameworks that identify and regenerate undesired dimensions to provide adequate accuracy with significantly lowered dimensionalities, thereby accelerating both the training and inference.
no code implementations • 7 Aug 2023 • Junyao Wang, Luke Chen, Mohammad Abdullah Al Faruque
With the rapid evolution of the Internet of Things, many real-world applications utilize heterogeneously connected sensors to capture time-series information.
no code implementations • 16 Jul 2023 • Mohanad Odema, Halima Bouzidi, Hamza Ouarnoughi, Smail Niar, Mohammad Abdullah Al Faruque
To achieve this, MaGNAS employs a two-tier evolutionary search to identify optimal GNNs and mapping pairings that yield the best performance trade-offs.
no code implementations • 27 Jun 2023 • Yifan Zhang, Arnav Vaibhav Malawade, Xiaofang Zhang, Yuhui Li, DongHwan Seong, Mohammad Abdullah Al Faruque, Sitao Huang
Autonomous systems (AS) are systems that can adapt and change their behavior in response to unanticipated events and include systems such as aerial drones, autonomous vehicles, and ground/aquatic robots.
1 code implementation • 17 Apr 2023 • Junyao Wang, Arnav Vaibhav Malawade, JunHong Zhou, Shih-Yuan Yu, Mohammad Abdullah Al Faruque
Effectively capturing intricate interactions among road users is of critical importance to achieving safe navigation for autonomous vehicles.
no code implementations • 14 Mar 2023 • Nafiul Rashid, Trier Mortlock, Mohammad Abdullah Al Faruque
SELF-CARE uses a learning-based classifier to process sensor features and model the environmental variations in sensing conditions known as the noise context.
no code implementations • 7 Mar 2023 • Mojtaba Taherisadr, Mohammad Abdullah Al Faruque, Salma Elmalaki
Thanks to the rapid growth in wearable technologies and recent advancement in machine learning and signal processing, monitoring complex human contexts becomes feasible, paving the way to develop human-in-the-loop IoT systems that naturally evolve to adapt to the human and environment state autonomously.
no code implementations • 24 Feb 2023 • Halima Bouzidi, Mohanad Odema, Hamza Ouarnoughi, Smail Niar, Mohammad Abdullah Al Faruque
Heterogeneous MPSoCs comprise diverse processing units of varying compute capabilities.
no code implementations • 24 Feb 2023 • Mohanad Odema, James Ferlez, Yasser Shoukry, Mohammad Abdullah Al Faruque
Runtime energy management has become quintessential for multi-sensor autonomous systems at the edge for achieving high performance given the platform constraints.
no code implementations • 13 Feb 2023 • Mohanad Odema, James Ferlez, Goli Vaisi, Yasser Shoukry, Mohammad Abdullah Al Faruque
To mitigate the high energy demand of Neural Network (NN) based Autonomous Driving Systems (ADSs), we consider the problem of offloading NN controllers from the ADS to nearby edge-computing infrastructure, but in such a way that formal vehicle safety properties are guaranteed.
1 code implementation • 6 Dec 2022 • Halima Bouzidi, Mohanad Odema, Hamza Ouarnoughi, Mohammad Abdullah Al Faruque, Smail Niar
Dynamic neural networks (DyNNs) have become viable techniques to enable intelligence on resource-constrained edge devices while maintaining computational efficiency.
no code implementations • 18 Jul 2022 • Luke Chen, Mohanad Odema, Mohammad Abdullah Al Faruque
Due to the high performance and safety requirements of self-driving applications, the complexity of modern autonomous driving systems (ADS) has been growing, instigating the need for more sophisticated hardware which could add to the energy footprint of the ADS platform.
no code implementations • 14 Jul 2022 • Rozhin Yasaei, Sina Faezi, Mohammad Abdullah Al Faruque
Moreover, a few existing HT localization methods have several weaknesses: reliance on a golden reference, inability to generalize for all types of HT, lack of scalability, low localization resolution, and manual feature engineering/property definition.
no code implementations • 24 May 2022 • Berken Utku Demirel, Luke Chen, Mohammad Abdullah Al Faruque
Providing health monitoring devices with machine intelligence is important for enabling automatic mobile healthcare applications.
no code implementations • 8 May 2022 • Nafiul Rashid, Trier Mortlock, Mohammad Abdullah Al Faruque
Detecting human stress levels and emotional states with physiological body-worn sensors is a complex task, but one with many health-related benefits.
no code implementations • 23 Feb 2022 • Arnav Vaibhav Malawade, Trier Mortlock, Mohammad Abdullah Al Faruque
Autonomous vehicles use multiple sensors, large deep-learning models, and powerful hardware platforms to perceive the environment and navigate safely.
1 code implementation • 17 Jan 2022 • Arnav Vaibhav Malawade, Trier Mortlock, Mohammad Abdullah Al Faruque
To address these limitations, we propose HydraFusion: a selective sensor fusion framework that learns to identify the current driving context and fuses the best combination of sensors to maximize robustness without compromising efficiency.
no code implementations • 15 Dec 2021 • Berken Utku Demirel, Islam Abdelsalam Bayoumy, Mohammad Abdullah Al Faruque
However, continuous monitoring of ECG signals is challenging in low-power wearable devices due to energy and memory constraints.
1 code implementation • 2 Sep 2021 • Arnav Vaibhav Malawade, Shih-Yuan Yu, Brandon Hsu, Harsimrat Kaeley, Anurag Karra, Mohammad Abdullah Al Faruque
The goal of roadscene2vec is to enable research into the applications and capabilities of road scene-graphs by providing tools for generating scene-graphs, graph learning models to generate spatio-temporal scene-graph embeddings, and tools for visualizing and analyzing scene-graph-based methodologies.
no code implementations • 2 Aug 2021 • Wenrui Lin, Berken Utku Demirel, Mohammad Abdullah Al Faruque, G. P. Li
The paper proposes accurate Blood Pressure Monitoring (BPM) based on a single-site Photoplethysmographic (PPG) sensor and provides an energy-efficient solution on edge cuffless wearable devices.
no code implementations • 2 Aug 2021 • Nafiul Rashid, Luke Chen, Manik Dautta, Abel Jimenez, Peter Tseng, Mohammad Abdullah Al Faruque
The recent advances in wearable devices have allowed the monitoring of several physiological signals related to stress.
no code implementations • 31 Jul 2021 • Berken Utku Demirel, Ivan Skelin, Haoxin Zhang, Jack J. Lin, Mohammad Abdullah Al Faruque
This paper proposes a novel lightweight method using the multitaper power spectrum to estimate arousal levels at wearable devices.
1 code implementation • 26 Jul 2021 • Shih-Yuan Yu, Rozhin Yasaei, Qingrong Zhou, Tommy Nguyen, Mohammad Abdullah Al Faruque
To attract more attention, we propose HW2VEC, an open-source graph learning tool that lowers the threshold for newcomers to research hardware security applications with graphs.
no code implementations • 22 Jul 2021 • Arnav Malawade, Mohanad Odema, Sebastien Lajeunesse-DeGroot, Mohammad Abdullah Al Faruque
We evaluate SAGE using an Nvidia Jetson TX2 and an industry-standard Nvidia Drive PX2 as the AV edge devices and demonstrate that our offloading strategy is practical for a wide range of DL models and internet connection bandwidths on 3G, 4G LTE, and WiFi technologies.
no code implementations • 20 Jul 2021 • Mohanad Odema, Nafiul Rashid, Berken Utku Demirel, Mohammad Abdullah Al Faruque
Edge-Cloud hierarchical systems employing intelligence through Deep Neural Networks (DNNs) endure the dilemma of workload distribution within them.
no code implementations • 19 Jul 2021 • Rozhin Yasaei, Shih-Yuan Yu, Emad Kasaeyan Naeini, Mohammad Abdullah Al Faruque
In this work, we propose a novel methodology, GNN4IP, to assess similarities between circuits and detect IP piracy.
no code implementations • 3 Feb 2021 • Nafiul Rashid, Berken Utku Demirel, Mohammad Abdullah Al Faruque
Unlike traditional early exit architecture that makes the exit decision based on classification confidence, AHAR proposes a novel adaptive architecture that uses an output block predictor to select a portion of the baseline architecture to use during the inference phase.
1 code implementation • 4 Jun 2019 • Shih Yuan Yu, Sujit Rokka Chhetri, Arquimedes Canedo, Palash Goyal, Mohammad Abdullah Al Faruque
Python library for knowledge graph embedding and representation learning.
no code implementations • 24 Aug 2018 • Jiang Wan, Blake S. Pollard, Sujit Rokka Chhetri, Palash Goyal, Mohammad Abdullah Al Faruque, Arquimedes Canedo
The digitalization of automation engineering generates large quantities of engineering data that is interlinked in knowledge graphs.