System-on-a-chip (SoC)-based Hardware Acceleration for Extreme Learning Machine

被引：0
|
作者：
Safaei, Amin [1 ]
Wu, Q. M. Jonathan [1 ]
Yang, Yimin [1 ]
Akilan, Thangarajah [1 ]
机构：
[1] Univ Windsor, Dept Elect & Comp Engn, Windsor, ON, Canada
来源：
2017 24TH IEEE INTERNATIONAL CONFERENCE ON ELECTRONICS, CIRCUITS AND SYSTEMS (ICECS) | 2017年
基金：
加拿大自然科学与工程研究理事会;
关键词：
Extreme learning machine; system on chip field-programmable gate array (SoC FPGA); hardware (HW) neural networks (NNs);
D O I：
暂无
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：
Extreme learning machine (ELM) is a popular class of supervised models in machine learning that is used in a wide range of applications, such as image object classification, video content analysis (VCA) and human action recognition. However, ELM classification is a computationally demanding task, and the existing hardware implementations are not efficient for embedded systems. This work addresses the implementation of extreme learning machine (ELM) in a system on a chip field-programmable gate-array (SoC FPGA)-based customized architecture to efficiently utilize hardware accelerator. The optimization process consists of parallelism extraction, algorithm tuning and deep pipelining.
引用
收藏
页码：470 / 473
页数：4
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