A scalable Echo State Networks hardware generator for embedded systems using high-level synthesis

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

Reservoir computing (RC) features with the rich computational dynamics is a kind of powerful machine learning paradigm that is well suited for non-linear time-series prediction and classification problems. However, this impressive performance comes with a cost of complex arithmetic operations and high memory usage that make it significantly challenging to deploy on embedded systems. Solutions based on CPU and/or GPU-based designs, provides flexibility but suffers from a lack of efficiency in terms of power, performance, and area (PPA). Although hardware-accelerated solutions can improve efficiency, it takes longer design cycles and is time-consuming. Furthermore, it may happen that design spec requires run change due to the fact that the network is retrained with the new data set to improve the performance. It leads to extra effort in the redesign of the hardware-accelerated solution. This preliminary work presents the design and implementation of a hardware generator for RC-ESNs (echo state networks) to tackle the problem. The proposed methodology is demonstrated by various offline-trained network parameters and topologies. Compared to existing solutions, the proposed framework provides scalability with the support of DSE in agile hardware design.

Original languageEnglish
Title of host publication2019 8th Mediterranean Conference on Embedded Computing, MECO 2019 - Proceedings
Number of pages6
PublisherIEEE
Publication date15. Jul 2019
Article number8760065
ISBN (Print)9781728117393
ISBN (Electronic)9781728117409
DOIs
Publication statusPublished - 15. Jul 2019
Event8th Mediterranean Conference on Embedded Computing, MECO 2019 - Budva, Montenegro
Duration: 10. Jun 201914. Jun 2019

Conference

Conference8th Mediterranean Conference on Embedded Computing, MECO 2019
Country/TerritoryMontenegro
CityBudva
Period10/06/201914/06/2019
SeriesMediterranean Conference on Embedded Computing, MECO
Volume2019
ISSN2377-5475

Keywords

  • Echo State Networks
  • Embedded Systems
  • Hardware Accelerator
  • High-Level Synthesis
  • Neural Networks
  • Reservoir Computing

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