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
5 papers · 1 filter
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…