45 citations · 214 across the 86 of their papers we have counts for
26 papers · 1 filter
ARMOR: Adaptive Resilience Against Model Poisoning Attacks in Continual Federated Learning for Mobile Indoor Localization
Danish Gufran, Akhil Singampalli, Sudeep Pasricha
Indoor localization has become increasingly essential for applications ranging from asset tracking to delivering personalized services. Federated learning (FL) offers a privacy-pre…
Unified Class and Domain Incremental Learning with Mixture of Experts for Indoor Localization
Akhil Singampalli, Sudeep Pasricha
Indoor localization using machine learning has gained traction due to the growing demand for location-based services. However, its long-term reliability is hindered by hardware/sof…
GATE: Graph Attention Neural Networks with Real-Time Edge Construction for Robust Indoor Localization using Mobile Embedded Devices
Danish Gufran, Sudeep Pasricha
Accurate indoor localization is crucial for enabling spatial context in smart environments and navigation systems. Wi-Fi Received Signal Strength (RSS) fingerprinting is a widely u…
Towards Explainable Indoor Localization: Interpreting Neural Network Learning on Wi-Fi Fingerprints Using Logic Gates
Danish Gufran, Sudeep Pasricha
Indoor localization using deep learning (DL) has demonstrated strong accuracy in mapping Wi-Fi RSS fingerprints to physical locations; however, most existing DL frameworks function…
DAILOC: Domain-Incremental Learning for Indoor Localization using Smartphones
Akhil Singampalli, Danish Gufran, Sudeep Pasricha
Wi-Fi fingerprinting-based indoor localization faces significant challenges in real-world deployments due to domain shifts arising from device heterogeneity and temporal variations…
SAFELOC: Overcoming Data Poisoning Attacks in Heterogeneous Federated Machine Learning for Indoor Localization
Akhil Singampalli, Danish Gufran, Sudeep Pasricha
Machine learning (ML) based indoor localization solutions are critical for many emerging applications, yet their efficacy is often compromised by hardware/software variations acros…
CAFEEN: A Cooperative Approach for Energy Efficient NoCs with Multi-Agent Reinforcement Learning
Kamil Khan, Sudeep Pasricha
In emerging high-performance Network-on-Chip (NoC) architectures, efficient power management is crucial to minimize energy consumption. We propose a novel framework called CAFEEN t…
AI and Machine Learning Driven Indoor Localization and Navigation with Mobile Embedded Systems
Sudeep Pasricha
Indoor navigation is a foundational technology to assist the tracking and localization of humans, autonomous vehicles, drones, and robots in indoor spaces. Due to the lack of penet…
SANGRIA: Stacked Autoencoder Neural Networks with Gradient Boosting for Indoor Localization
Danish Gufran, Saideep Tiku, Sudeep Pasricha
Indoor localization is a critical task in many embedded applications, such as asset tracking, emergency response, and realtime navigation. In this article, we propose a novel finge…
STELLAR: Siamese Multi-Headed Attention Neural Networks for Overcoming Temporal Variations and Device Heterogeneity with Indoor Localization
Danish Gufran, Saideep Tiku, Sudeep Pasricha
Smartphone-based indoor localization has emerged as a cost-effective and accurate solution to localize mobile and IoT devices indoors. However, the challenges of device heterogenei…
CALLOC: Curriculum Adversarial Learning for Secure and Robust Indoor Localization
Danish Gufran, Sudeep Pasricha
Indoor localization has become increasingly vital for many applications from tracking assets to delivering personalized services. Yet, achieving pinpoint accuracy remains a challen…
FedHIL: Heterogeneity Resilient Federated Learning for Robust Indoor Localization with Mobile Devices
Danish Gufran, Sudeep Pasricha
Indoor localization plays a vital role in applications such as emergency response, warehouse management, and augmented reality experiences. By deploying machine learning (ML) based…
TRON: Transformer Neural Network Acceleration with Non-Coherent Silicon Photonics
Salma Afifi, Febin Sunny, Mahdi Nikdast +1
Transformer neural networks are rapidly being integrated into state-of-the-art solutions for natural language processing (NLP) and computer vision. However, the complex structure o…
Cross-Layer Design for AI Acceleration with Non-Coherent Optical Computing
Febin Sunny, Mahdi Nikdast, Sudeep Pasricha
Emerging AI applications such as ChatGPT, graph convolutional networks, and other deep neural networks require massive computational resources for training and inference. Contempor…
MOELA: A Multi-Objective Evolutionary/Learning Design Space Exploration Framework for 3D Heterogeneous Manycore Platforms
Sirui Qi, Yingheng Li, Sudeep Pasricha +1
To enable emerging applications such as deep machine learning and graph processing, 3D network-on-chip (NoC) enabled heterogeneous manycore platforms that can integrate many proces…
Adversarial Attacks on Machine Learning in Embedded and IoT Platforms
Christian Westbrook, Sudeep Pasricha
Machine learning (ML) algorithms are increasingly being integrated into embedded and IoT systems that surround us, and they are vulnerable to adversarial attacks. The deployment of…
RecLight: A Recurrent Neural Network Accelerator with Integrated Silicon Photonics
Febin Sunny, Mahdi Nikdast, Sudeep Pasricha
Recurrent Neural Networks (RNNs) are used in applications that learn dependencies in data sequences, such as speech recognition, human activity recognition, and anomaly detection.…
Robust Perception Architecture Design for Automotive Cyber-Physical Systems
Joydeep Dey, Sudeep Pasricha
In emerging automotive cyber-physical systems (CPS), accurate environmental perception is critical to achieving safety and performance goals. Enabling robust perception for vehicle…
Siamese Neural Encoders for Long-Term Indoor Localization with Mobile Devices
Saideep Tiku, Sudeep Pasricha
Fingerprinting-based indoor localization is an emerging application domain for enhanced positioning and tracking of people and assets within indoor locales. The superior pairing of…
TENET: Temporal CNN with Attention for Anomaly Detection in Automotive Cyber-Physical Systems
S. V. Thiruloga, V. K. Kukkala, S. Pasricha
Modern vehicles have multiple electronic control units (ECUs) that are connected together as part of a complex distributed cyber-physical system (CPS). The ever-increasing communic…
SONIC: A Sparse Neural Network Inference Accelerator with Silicon Photonics for Energy-Efficient Deep Learning
Febin Sunny, Mahdi Nikdast, Sudeep Pasricha
Sparse neural networks can greatly facilitate the deployment of neural networks on resource-constrained platforms as they offer compact model sizes while retaining inference accura…
LATTE: LSTM Self-Attention based Anomaly Detection in Embedded Automotive Platforms
Vipin K. Kukkala, Sooryaa V. Thiruloga, Sudeep Pasricha
Modern vehicles can be thought of as complex distributed embedded systems that run a variety of automotive applications with real-time constraints. Recent advances in the automotiv…
ROBIN: A Robust Optical Binary Neural Network Accelerator
Febin P. Sunny, Asif Mirza, Mahdi Nikdast +1
Domain specific neural network accelerators have garnered attention because of their improved energy efficiency and inference performance compared to CPUs and GPUs. Such accelerato…
CHISEL: Compression-Aware High-Accuracy Embedded Indoor Localization with Deep Learning
Liping Wang, Saideep Tiku, Sudeep Pasricha
GPS technology has revolutionized the way we localize and navigate outdoors. However, the poor reception of GPS signals in buildings makes it unsuitable for indoor localization. Wi…