Details

Autor(en) / Beteiligte

• Desoli, Giuseppe
• Chawla, Nitin
• Boesch, Thomas
• Singh, Surinder-pal
• Guidetti, Elio
• De Ambroggi, Fabio
• Majo, Tommaso
• Zambotti, Paolo
• Ayodhyawasi, Manuj
• Singh, Harvinder
• Aggarwal, Nalin

Titel

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

Ist Teil von

• 2017 IEEE International Solid-State Circuits Conference (ISSCC), 2017, p.238-239

Ort / Verlag

IEEE

Erscheinungsjahr

2017

Link zum Volltext

• Volltext

Quelle

IEEE/IET Electronic Library (IEL)

Beschreibungen/Notizen

• 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.