publication . Conference object . 2016

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

Antonio Pullini; Francesco Conti; Davide Rossi; Igor Loi; Michael Gautschi; Luca Benini;
Restricted English
  • Published: 01 Jan 2016
  • Publisher: IEEE
  • 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...
Persistent Identifiers
Subjects
free text keywords: Electrical and Electronic Engineering, System on a chip, Multi-core processor, Computer engineering, Efficient energy use, Embedded system, business.industry, business, Server, Kernel (linear algebra), Hardware acceleration, Convolutional neural network, Wearable computer, 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
Validated by funder
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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