activity
20182022
most citedGeneralized Key-Value Memory to Flexibly Adjust Redundancy in Memory-Augmented Networks

9 citations · 18 across the 8 of their papers we have counts for

collaborators

13 papers

cs.LG20229 cited

Generalized Key-Value Memory to Flexibly Adjust Redundancy in Memory-Augmented Networks

Denis Kleyko, Geethan Karunaratne, Jan M. Rabaey +2

Memory-augmented neural networks enhance a neural network with an external key-value memory whose complexity is typically dominated by the number of support vectors in the key memo…

cs.OH20222 cited

Innovating at Speed and at Scale: A Next Generation Infrastructure for Accelerating Semiconductor Technologies

Richard A. Gottscho, Edlyn V. Levine, Tsu-Jae King Liu +7

Semiconductor innovation drives improvements to technologies that are critical to modern society. The country that successfully accelerates semiconductor innovation is positioned t…

cs.CR2021

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…

cs.ET2021

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…

cs.LG20215 cited

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…

cs.LG2021

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…