Bio-inspired system architecture for energy efficient, BIGDATA computing with application to wide area motion imagery

Andreou, Andreas G.; Figliolia, Tomas; Sanni, Kayode; Murray, Thomas G.; Tognetti, Gaspar; Mendat, Daniel R.; Molin, Jamal Lottier; Villemur, Martin; Pouliquen, Philippe O.; Julián, P.; Etienne‐Cummings, Ralph; Doxas, I.

doi:10.1109/lascas.2016.7450995

Cited by 6 publications

(2 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The vast activity noted in the literature is a clear representation of the technical advances attained lately with bioinspired computation applied to Big Data. Indeed, manifold domains have capitalized bio-inspired computation in databased applications, including energy [198,199], transport and mobility [60], health [200], industry [201,202], agriculture [203], cyber-physical systems [20], social networks [204][205][206] or sensor networks [207], among many others.…”

Section: Critical Analysis Open Challenges and Research Directionsmentioning

confidence: 99%

Bio-inspired computation for big data fusion, storage, processing, learning and visualization: state of the art and future directions

Torre-Bastida

Díaz-de-Arcaya

Osaba

et al. 2021

Neural Comput & Applic

View full text Add to dashboard Cite

This overview gravitates on research achievements that have recently emerged from the confluence between Big Data technologies and bio-inspired computation. A manifold of reasons can be identified for the profitable synergy between these two paradigms, all rooted on the adaptability, intelligence and robustness that biologically inspired principles can provide to technologies aimed to manage, retrieve, fuse and process Big Data efficiently. We delve into this research field by first analyzing in depth the existing literature, with a focus on advances reported in the last few years. This prior literature analysis is complemented by an identification of the new trends and open challenges in Big Data that remain unsolved to date, and that can be effectively addressed by bio-inspired algorithms. As a second contribution, this work elaborates on how bio-inspired algorithms need to be adapted for their use in a Big Data context, in which data fusion becomes crucial as a previous step to allow processing and mining several and potentially heterogeneous data sources. This analysis allows exploring and comparing the scope and efficiency of existing approaches across different problems and domains, with the purpose of identifying new potential applications and research niches. Finally, this survey highlights open issues that remain unsolved to date in this research avenue, alongside a prescription of recommendations for future research.

show abstract

Section: Critical Analysis Open Challenges and Research Directionsmentioning

confidence: 99%

Bio-inspired computation for big data fusion, storage, processing, learning and visualization: state of the art and future directions

Torre-Bastida

Díaz-de-Arcaya

Osaba

et al. 2021

Neural Comput & Applic

View full text Add to dashboard Cite

show abstract

“…Energy aware embedded visual processing is necessary for systems where endowing cameras with local processing capability is crucial for operational autonomy, such as in autonomous or semi‐autonomous vehicle navigation [1], processing data from wide area motion imagery [2], and feature‐driven learning/intelligent processing in intelligent Internet of things [3] (

I^{2}

oT). IoT devices are often thought as the ‘edge’ of a large sophisticated cloud processing infrastructure.…”

Section: Introductionmentioning

confidence: 99%

Energy aware simplicial processor for embedded morphological visual processing in intelligent internet of things

2018

View full text Add to dashboard Cite

This Letter presents the architecture implementation and testing of an single instruction multiple data (SIMD) processor for energy aware embedded morphological visual processing using the simplicial piece-wise linear approximation. The architecture comprises a linear array of 48 × 48 processing elements, each connected to an eightneighbour clique operating on binary input and state data. The architecture is synthesised from a custom designed ultra low-voltage CMOS library and fabricated in a 55 nm CMOS technology. The chip is capable of dynamic voltage/frequency scaling with power supplies between 0.5 and 1.2 V. The fabricated chip achieves an overall performance of 293 TOPS/W with dynamic energy dissipation efficiency of 3.4 fJ per output operation at 0.6 V.

show abstract

Parallelism Analysis for a Multi-core Speech Recognition Architecture

Pasciaroni

Julián

Andreou

2019

2019 Argentine Conference on Electronics (CAE)

View full text Add to dashboard Cite

Bio-inspired system architecture for energy efficient, BIGDATA computing with application to wide area motion imagery

Cited by 6 publications

References 33 publications

Bio-inspired computation for big data fusion, storage, processing, learning and visualization: state of the art and future directions

Bio-inspired computation for big data fusion, storage, processing, learning and visualization: state of the art and future directions

Energy aware simplicial processor for embedded morphological visual processing in intelligent internet of things

Parallelism Analysis for a Multi-core Speech Recognition Architecture

Contact Info

Product

Resources

About