45 citations · 214 across the 86 of their papers we have counts for
7 papers · 2 filters
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…