UZH-Logo

Maintenance Infos

Computing spike-based convolutions on GPUs


Nageswaran, J M; Dutt, N; Wang, Y; Delbrueck, T (2009). Computing spike-based convolutions on GPUs. In: IEEE International Symposium on Circuits and Systems, 2009 (ISCAS 2009), Taipei, Taiwan, 24 May 2009 - 27 May 2009, 1917-1920.

Abstract

In spiking neural networks, asynchronous spike events are processed in parallel by neurons. Emulations of such networks are traditionally computed by CPUs or realized using dedicated neuromorphic hardware. In many neuromorphic systems, the address-event-representation (AER) is used for spike communication. In this paper we present the acceleration of AER based spike processing using a graphics processing unit (GPU). In our experiment we interface a 128times128 pixel AER vision sensor to a spiking neural network implemented on a GPU for real-time convolution-based nonlinear feature extraction with convolution kernel sizes ranging from 48times48 to 112times112 pixels. We show parallelism-performance trade-offs on GPUs for single spike per thread, multiple spikes per thread, and multiple objects parallelism techniques. Our implementation can achieve a kernel speedup of up to 35times on a single NVIDIA GTX280 board when compared to a CPU-only implementation.

In spiking neural networks, asynchronous spike events are processed in parallel by neurons. Emulations of such networks are traditionally computed by CPUs or realized using dedicated neuromorphic hardware. In many neuromorphic systems, the address-event-representation (AER) is used for spike communication. In this paper we present the acceleration of AER based spike processing using a graphics processing unit (GPU). In our experiment we interface a 128times128 pixel AER vision sensor to a spiking neural network implemented on a GPU for real-time convolution-based nonlinear feature extraction with convolution kernel sizes ranging from 48times48 to 112times112 pixels. We show parallelism-performance trade-offs on GPUs for single spike per thread, multiple spikes per thread, and multiple objects parallelism techniques. Our implementation can achieve a kernel speedup of up to 35times on a single NVIDIA GTX280 board when compared to a CPU-only implementation.

Citations

Altmetrics

Downloads

54 downloads since deposited on 13 Mar 2010
36 downloads since 12 months
Detailed statistics

Additional indexing

Item Type:Conference or Workshop Item (Speech), refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Neuroinformatics
Dewey Decimal Classification:570 Life sciences; biology
Language:English
Event End Date:27 May 2009
Deposited On:13 Mar 2010 11:00
Last Modified:05 Apr 2016 14:00
ISBN:978-1-4244-3827-3
Additional Information:© 2009 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Publisher DOI:10.1109/ISCAS.2009.5118157
Related URLs:http://conf.ncku.edu.tw/iscas2009/ (Organisation)
http://ieeexplore.ieee.org (Publisher)
http://www.ini.uzh.ch/node/24220
Permanent URL: http://doi.org/10.5167/uzh-32036

Download

[img]
Preview
Filetype: PDF
Size: 1MB
View at publisher

TrendTerms

TrendTerms displays relevant terms of the abstract of this publication and related documents on a map. The terms and their relations were extracted from ZORA using word statistics. Their timelines are taken from ZORA as well. The bubble size of a term is proportional to the number of documents where the term occurs. Red, orange, yellow and green colors are used for terms that occur in the current document; red indicates high interlinkedness of a term with other terms, orange, yellow and green decreasing interlinkedness. Blue is used for terms that have a relation with the terms in this document, but occur in other documents.
You can navigate and zoom the map. Mouse-hovering a term displays its timeline, clicking it yields the associated documents.

Author Collaborations