2.3k citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- ETH ZurichCH61 papers
- University of ZurichCH59 papers
- SIB Swiss Institute of BioinformaticsCH13 papers
- Forschungszentrum JülichDE4 papers
- Italian Institute of TechnologyIT4 papers
- Universidad de SevillaES4 papers
- Intel (United States)US3 papers
- Swiss Federal Institute of Aquatic Science and TechnologyCH3 papers
- University of BernCH3 papers
- Accenture (United States)US2 papers
- Bielefeld UniversityDE2 papers
- California Institute of TechnologyUS2 papers
66 papers
Comparing apples to apples -- Using a modular and adaptable analysis pipeline to compare slow cerebral rhythms across heterogeneous datasets
Robin Gutzen, Giulia De Bonis, Chiara De Luca +11
Neuroscience is moving towards a more integrative discipline, where understanding brain function requires consolidating the accumulated evidence seen across experiments, species, a…
Visual Odometry with Neuromorphic Resonator Networks
Alpha Renner, Lazar Supic, Andreea Danielescu +4
Visual Odometry (VO) is a method to estimate self-motion of a mobile robot using visual sensors. Unlike odometry based on integrating differential measurements that can accumulate…
Neuromorphic Visual Scene Understanding with Resonator Networks
Alpha Renner, Lazar Supic, Andreea Danielescu +5
Analyzing a visual scene by inferring the configuration of a generative model is widely considered the most flexible and generalizable approach to scene understanding. Yet, one maj…
Continuous-Time Analog Filters for Audio Edge Intelligence: Review on Circuit Designs
Kwantae Kim, Shih-Chii Liu
Edge audio devices can reduce data bandwidth requirements by pre-processing input speech on the device before transmission to the cloud. As edge devices are required to ensure alwa…
Beyond backpropagation: bilevel optimization through implicit differentiation and equilibrium propagation
Nicolas Zucchet, João Sacramento
This paper reviews gradient-based techniques to solve bilevel optimization problems. Bilevel optimization is a general way to frame the learning of systems that are implicitly defi…
Skydiver: A Spiking Neural Network Accelerator Exploiting Spatio-Temporal Workload Balance
Qinyu Chen, Chang Gao, Xinyuan Fang +1
Spiking Neural Networks (SNNs) are developed as a promising alternative to Artificial Neural networks (ANNs) due to their more realistic brain-inspired computing models. SNNs have…
A 120dB Programmable-Range On-Chip Pulse Generator for Characterizing Ferroelectric Devices
Shyam Narayanan, Erika Covi, Viktor Havel +7
Novel non-volatile memory devices based on ferroelectric thin films represent a promising emerging technology that is ideally suited for neuromorphic applications. The physical swi…
Modeling the Repetition-based Recovering of Acoustic and Visual Sources with Dendritic Neurons
Giorgia Dellaferrera, Toshitake Asabuki, Tomoki Fukai
In natural auditory environments, acoustic signals originate from the temporal superimposition of different sound sources. The problem of inferring individual sources from ambiguou…
Spartus: A 9.4 TOp/s FPGA-based LSTM Accelerator Exploiting Spatio-Temporal Sparsity
Chang Gao, Tobi Delbruck, Shih-Chii Liu
Long Short-Term Memory (LSTM) recurrent networks are frequently used for tasks involving time-sequential data such as speech recognition. Unlike previous LSTM accelerators that eit…
The Backpropagation Algorithm Implemented on Spiking Neuromorphic Hardware
Alpha Renner, Forrest Sheldon, Anatoly Zlotnik +2
The capabilities of natural neural systems have inspired new generations of machine learning algorithms as well as neuromorphic very large-scale integrated (VLSI) circuits capable…
Bottom-up and top-down approaches for the design of neuromorphic processing systems: Tradeoffs and synergies between natural and artificial intelligence
Charlotte Frenkel, David Bol, Giacomo Indiveri
While Moore's law has driven exponential computing power expectations, its nearing end calls for new avenues for improving the overall system performance. One of these avenues is t…
Robust neuromorphic coupled oscillators for adaptive pacemakers
Renate Krause, Joanne J. A. van Bavel, Chenxi Wu +3
Neural coupled oscillators are a useful building block in numerous models and applications. They were analyzed extensively in theoretical studies and more recently, in biologically…
Supervised training of spiking neural networks for robust deployment on mixed-signal neuromorphic processors
Julian Büchel, Dmitrii Zendrikov, Sergio Solinas +2
Mixed-signal analog/digital circuits emulate spiking neurons and synapses with extremely high energy efficiency, an approach known as "neuromorphic engineering". However, analog ci…
Adaptive Extreme Edge Computing for Wearable Devices
Erika Covi, Elisa Donati, Hadi Heidari +4
Wearable devices are a fast-growing technology with impact on personal healthcare for both society and economy. Due to the widespread of sensors in pervasive and distributed networ…
An electronic neuromorphic system for real-time detection of High Frequency Oscillations (HFOs) in intracranial EEG
Mohammadali Sharifshazileh, Karla Burelo, Johannes Sarnthein +1
In this work, we present a neuromorphic system that combines for the first time a neural recording headstage with a signal-to-spike conversion circuit and a multi-core spiking neur…
Closed-loop spiking control on a neuromorphic processor implemented on the iCub
Jingyue Zhao, Nicoletta Risi, Marco Monforte +3
Despite neuromorphic engineering promises the deployment of low latency, adaptive and low power systems that can lead to the design of truly autonomous artificial agents, the devel…
Learning-based Defect Recognition for Quasi-Periodic Microscope Images
Nik Dennler, Antonio Foncubierta-Rodriguez, Titus Neupert +1
Controlling crystalline material defects is crucial, as they affect properties of the material that may be detrimental or beneficial for the final performance of a device. Defect a…
Visual Pattern Recognition with on On-chip Learning: towards a Fully Neuromorphic Approach
Sandro Baumgartner, Alpha Renner, Raphaela Kreiser +3
We present a spiking neural network (SNN) for visual pattern recognition with on-chip learning on neuromorphichardware. We show how this network can learn simple visual patterns co…
Ultra-Low-Power FDSOI Neural Circuits for Extreme-Edge Neuromorphic Intelligence
Arianna Rubino, Can Livanelioglu, Ning Qiao +2
Recent years have seen an increasing interest in the development of artificial intelligence circuits and systems for edge computing applications. In-memory computing mixed-signal n…
Hardware Implementation of Deep Network Accelerators Towards Healthcare and Biomedical Applications
Mostafa Rahimi Azghadi, Corey Lammie, Jason K. Eshraghian +4
The advent of dedicated Deep Learning (DL) accelerators and neuromorphic processors has brought on new opportunities for applying both Deep and Spiking Neural Network (SNN) algorit…
Second-order Phase Transition in Phytoplankton Trait Dynamics
Jenny Held, Tom Lorimer, Francesco Pomati +2
Key traits of unicellular species, like cell size, often follow scale-free or self-similar distributions, hinting at the possibility of an underlying critical process. However, lin…
Redesigning SLAM for Arbitrary Multi-Camera Systems
Juichung Kuo, Manasi Muglikar, Zichao Zhang +1
Adding more cameras to SLAM systems improves robustness and accuracy but complicates the design of the visual front-end significantly. Thus, most systems in the literature are tail…
Learning to Exploit Multiple Vision Modalities by Using Grafted Networks
Yuhuang Hu, Tobi Delbruck, Shih-Chii Liu
Novel vision sensors such as thermal, hyperspectral, polarization, and event cameras provide information that is not available from conventional intensity cameras. An obstacle to u…
Learning Depth With Very Sparse Supervision
Antonio Loquercio, Alexey Dosovitskiy, Davide Scaramuzza
Motivated by the astonishing capabilities of natural intelligent agents and inspired by theories from psychology, this paper explores the idea that perception gets coupled to 3D pr…