172 citations · 425 across the 18 of their papers we have counts for
6 papers · 1 filter
A low-overhead approach for self-sovereign identity in IoT
Geovane Fedrecheski, Laisa C. P. Costa, Samira Afzal +3
We present a low-overhead mechanism for self-sovereign identification and communication of IoT agents in constrained networks. Our main contribution is to enable native use of Dece…
Generalized Learning Vector Quantization for Classification in Randomized Neural Networks and Hyperdimensional Computing
Cameron Diao, Denis Kleyko, Jan M. Rabaey +1
Machine learning algorithms deployed on edge devices must meet certain resource constraints and efficiency requirements. Random Vector Functional Link (RVFL) networks are favored f…
Vector Symbolic Architectures as a Computing Framework for Emerging Hardware
Denis Kleyko, Mike Davies, E. Paxon Frady +8
This article reviews recent progress in the development of the computing framework vector symbolic architectures (VSA) (also known as hyperdimensional computing). This framework is…
Efficient emotion recognition using hyperdimensional computing with combinatorial channel encoding and cellular automata
Alisha Menon, Anirudh Natarajan, Reva Agashe +5
In this paper, a hardware-optimized approach to emotion recognition based on the efficient brain-inspired hyperdimensional computing (HDC) paradigm is proposed. Emotion recognition…
Memory-Efficient, Limb Position-Aware Hand Gesture Recognition using Hyperdimensional Computing
Andy Zhou, Rikky Muller, Jan Rabaey
Electromyogram (EMG) pattern recognition can be used to classify hand gestures and movements for human-machine interface and prosthetics applications, but it often faces reliabilit…
Sparse-Push: Communication- & Energy-Efficient Decentralized Distributed Learning over Directed & Time-Varying Graphs with non-IID Datasets
Sai Aparna Aketi, Amandeep Singh, Jan Rabaey
Current deep learning (DL) systems rely on a centralized computing paradigm which limits the amount of available training data, increases system latency, and adds privacy and secur…