Time-Coded Spiking Fourier Transform in Neuromorphic Hardware-Reference-Cited by-同舟云学术

Time-Coded Spiking Fourier Transform in Neuromorphic Hardware

Published:2022-11-01 Issue:11 Volume:71 Page:2792-2802
ISSN:0018-9340
Container-title:IEEE Transactions on Computers
language:
Short-container-title:IEEE Trans. Comput.

Author:

Lopez-Randulfe Javier¹^ORCID,Reeb Nico¹,Karimi Negin¹^ORCID,Liu Chen²^ORCID,Gonzalez Hector A.²^ORCID,Dietrich Robin¹^ORCID,Vogginger Bernhard²^ORCID,Mayr Christian²^ORCID,Knoll Alois¹^ORCID

Affiliation:

1. Department of Informatics, Technical University of Munich, Munich, Germany

2. Faculty of Electrical and Computer Engineering, Technische Universität Dresden, Dresden, Germany

Funder

Federal Ministry of Education and Research of Germany

Federal Ministry of Education and Research of Germany in the programme of Souverän Digital Vernetzt

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computational Theory and Mathematics,Hardware and Architecture,Theoretical Computer Science,Software

Link

http://xplorestaging.ieee.org/ielx7/12/9913765/09743682.pdf?arnumber=9743682

Reference32 articles.

1. A reconfigurable on-line learning spiking neuromorphic processor comprising 256 neurons and 128K synapses

2. A Scalable Multicore Architecture With Heterogeneous Memory Structures for Dynamic Neuromorphic Asynchronous Processors (DYNAPs)

3. Braindrop: A Mixed-Signal Neuromorphic Architecture With a Dynamical Systems-Based Programming Model

4. Unsupervised learning from continuous video in a scalable predictive recurrent network;piekniewski,2016

5. SiamSNN: Spike-based Siamese network for energy-efficient and real-time object tracking;luo,2020

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