publication . Conference object . 2016

A heterogeneous multi-core system-on-chip for energy efficient brain inspired vision

Pullini, Antonio; Conti, Francesco; Rossi, Davide; Loi, Igor; Gautschi, Michael; Benini, Luca;
Closed Access English
  • Published: 01 Jan 2016
  • Publisher: Institute of Electrical and Electronics Engineers Inc.
  • Country: Italy
Abstract
Computer vision (CV) based on Convolutional Neural Networks (CNN) is a rapidly developing field thanks to CNN's flexibility, strong generalization capability and classification accuracy (matching and sometimes exceeding human performance). CNN-based classifiers are typically deployed on servers or high-end embedded platforms. However, their ability to “compress” low information density data such as images into highly informative classification tags makes them extremely interesting for wearable and IoT scenarios, should it be possible to fit their computational requirements within deeply embedded devices such as visual sensor nodes. We propose a 65nm system-on-ch...
Subjects
free text keywords: Electrical and Electronic Engineering, Multi-core processor, System on a chip, Kernel (linear algebra), Convolutional neural network, Electronic engineering, Wearable computer, Hardware acceleration, Server, Efficient energy use, Computer engineering, Embedded system, business.industry, business, Computer science
Funded by
EC| ExaNoDe
Project
ExaNoDe
European Exascale Processor Memory Node Design
  • Funder: European Commission (EC)
  • Project Code: 671578
  • Funding stream: H2020 | RIA
Communities
FET H2020FET HPC: HPC Core Technologies, Programming Environments and Algorithms for Extreme Parallelism and Extreme Data Applications
FET H2020FET HPC: European Exascale Processor Memory Node Design
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publication . Conference object . 2016

A heterogeneous multi-core system-on-chip for energy efficient brain inspired vision

Pullini, Antonio; Conti, Francesco; Rossi, Davide; Loi, Igor; Gautschi, Michael; Benini, Luca;