1 citations · 1 across the 8 of their papers we have counts for
8 papers
Superposed Latent Autoencoder
Quanling Zhao, Jiaying Yang, Tianqi Zhang +4
Autoencoders typically meet tight latent-memory budgets by making each latent representation smaller, sacrificing representational capacity. We ask a different question: can multip…
HyperLiDAR: Adaptive Post-Deployment LiDAR Segmentation via Hyperdimensional Computing
Ivannia Gomez Moreno, Yi Yao, Ye Tian +7
LiDAR semantic segmentation plays a pivotal role in 3D scene understanding for edge applications such as autonomous driving. However, significant challenges remain for real-world d…
QMC: Efficient SLM Edge Inference via Outlier-Aware Quantization and Emergent Memories Co-Design
Nilesh Prasad Pandey, Jangseon Park, Onat Gungor +2
Deploying Small Language Models (SLMs) on edge platforms is critical for real-time, privacy-sensitive generative AI, yet constrained by memory, latency, and energy budgets. Quantiz…
FedUHD: Unsupervised Federated Learning using In-Memory Hyperdimensional Computing
You Hak Lee, Xiaofan Yu, Keming Fan +4
Unsupervised federated learning (UFL) enables privacy-preserving distributed training without data labeling, yet practical deployment remains challenging due to non-IID data, high…
DPQ-HD: Post-Training Compression for Ultra-Low Power Hyperdimensional Computing
Nilesh Prasad Pandey, Shriniwas Kulkarni, David Wang +3
Hyperdimensional Computing (HDC) is emerging as a promising approach for edge AI, offering a balance between accuracy and efficiency. However, current HDC-based applications often…
Offload Rethinking by Cloud Assistance for Efficient Environmental Sound Recognition on LPWANs
Le Zhang, Quanling Zhao, Run Wang +4
Learning-based environmental sound recognition has emerged as a crucial method for ultra-low-power environmental monitoring in biological research and city-scale sensing systems. T…
E-QUARTIC: Energy Efficient Edge Ensemble of Convolutional Neural Networks for Resource-Optimized Learning
Le Zhang, Onat Gungor, Flavio Ponzina +1
Ensemble learning is a meta-learning approach that combines the predictions of multiple learners, demonstrating improved accuracy and robustness. Nevertheless, ensembling models li…
MicroHD: An Accuracy-Driven Optimization of Hyperdimensional Computing Algorithms for TinyML systems
Flavio Ponzina, Tajana Rosing
Hyperdimensional computing (HDC) is emerging as a promising AI approach that can effectively target TinyML applications thanks to its lightweight computing and memory requirements.…