Work

Scene Context Classification with Event-Driven Spiking Deep Neural Networks

Year: 2018

Type: conference-paper

Abstract: Event-Driven computation is attracting growing attention among researchers for several reasons. On one hand, the availability of new bio-inspired retina-like vision sensors that provide spiking outputs, like the Dynamic Vision Sensor (DVS) make it possible to demonstrate energy efficient and high-speed complex vision tasks. On the other hand, the emergence of abundant new nanoscale devices that operate as tunable two-terminal resistive elements, which when operated through dynamic pulsing techniques emulate learning and processing in the brain, promise an explosion of highly compact energy efficient neuromorphic event-driven applications. In this paper we focus for the first time on a high-level cognitive task, namely scene context classification, performed by event-driven computations and using real sensory data from a DVS camera.

Source: IEEE International Conference on Electronics, Circuits and Systems (ICECS)

DOI: 10.1109/icecs.2018.8617982

Authors: Pablo Negri, Miguel Soto, Bernabe Linares-Barranco, Teresa Serrano-Gotarredona

Institutions: Consejo Nacional de Investigaciones Científicas y Técnicas, Universidad de Buenos Aires, Instituto de Microelectrónica de Sevilla, Universidad de Sevilla

Language: English

Topic: Advanced Memory and Neural Computing

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: green

Funders: Horizon 2020 Framework Programme, European Regional Development Fund

Awards: G3174821817, G4412364701, G4951502333