Deep Convolutional Network: an Event-based approach

被引：0

作者：

Gigena Ivanovich, D. ^{[1
]}

Rodriguez, N. ^{[1
]}

Pasciaroni, A. ^{[1
]}

Julian, P. ^{[2
]}

机构：

[1] IIIE CONICET, Bahia Blanca, Buenos Aires, Argentina

[2] Silicon Austria Labs, Linz, Austria

来源：

2021 ARGENTINE CONFERENCE ON ELECTRONICS (CAE 2021) | 2021年

关键词：

Deep Neural network; CNN; event-based imager;

D O I：

10.1109/CAE51562.2021.9397561

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

In this paper, we propose a methodology to incrementally compute a convolutional layer of a neural network based on events, and an architecture that can efficiently implement it. In order to illustrate the approach, we present an application example, where we train a traditional DNN based on a LeNet architecture using a traffic signs dataset and then, we replace the first convolutional layer with our event based approach.

引用

页码：50 / 54

页数：5

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