14.1 A 2.9TOPS/W deep convolutional neural network SoC in FD-SOI 28nm for intelligent embedded systems

A booming number of computer vision, speech recognition, and signal processing applications, are increasingly benefiting from the use of deep convolutional neural networks (DCNN) stemming from the seminal work of Y. LeCun et al. [1] and others that led to winning the 2012 ImageNet Large Scale Visual...

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Bibliographic Details
Published in2017 IEEE International Solid-State Circuits Conference (ISSCC) pp. 238 - 239
Main Authors Desoli, Giuseppe, Chawla, Nitin, Boesch, Thomas, Singh, Surinder-pal, Guidetti, Elio, De Ambroggi, Fabio, Majo, Tommaso, Zambotti, Paolo, Ayodhyawasi, Manuj, Singh, Harvinder, Aggarwal, Nalin
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.02.2017
Subjects
Convolution
Digital signal processing
Kernel
Memory management
Neural networks
Power demand
Throughput
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Summary:A booming number of computer vision, speech recognition, and signal processing applications, are increasingly benefiting from the use of deep convolutional neural networks (DCNN) stemming from the seminal work of Y. LeCun et al. [1] and others that led to winning the 2012 ImageNet Large Scale Visual Recognition Challenge with AlexNet [2], a DCNN significantly outperforming classical approaches for the first time. In order to deploy these technologies in mobile and wearable devices, hardware acceleration plays a critical role for real-time operation with very limited power consumption and with embedded memory overcoming the limitations of fully programmable solutions.
ISSN:2376-8606
DOI:10.1109/ISSCC.2017.7870349