Advanced technologies for brain-inspired computing

F. Clermidy (1) , R. Heliot (1) , A. Valentian (1) , C. Gamrat (2) , O. Bichler (2) , M. Duranton (2) , Bilel Blehadj (3) , Olivier Temam (3)

1 CEA-LETI - Commissariat à l'énergie atomique et aux énergies alternatives - Laboratoire d'Electronique et de Technologie de l'Information
2 DACLE-LIST - Département d'Architectures, Conception et Logiciels Embarqués-LIST
3 Inria Saclay - Ile de France

This paper aims at presenting how new technologies can overcome classical implementation issues of Neural Networks. Resistive memories such as Phase Change Memories and Conductive-Bridge RAM can be used for obtaining low-area synapses thanks to programmable resistance also called Memristors. Similarly, the high capacitance of Through Silicon Vias can be used to greatly improve analog neurons and reduce their area. The very same devices can also be used for improving connectivity of Neural Networks as demonstrated by an application. Finally, some perspectives are given on the usage of 3D monolithic integration for better exploiting the third dimension and thus obtaining systems closer to the brain.

Keywords: Monolithic integrated circuits; Computer aided design; Through silicon vias; Neural networks; Brain-inspired computing; Random access storage; Analog neurons; Resistive memory; Monolithic integration; Advanced technology

Domains: Engineering Sciences [physics]; Computer Science [cs]

F. Clermidy, R. Heliot, A. Valentian, C. Gamrat, O. Bichler, et al.. Advanced technologies for brain-inspired computing. 2014 19th Asia and South Pacific Design Automation Conference (ASP-DAC), Jan 2014, Suntec, Singapore. pp.563-569, ⟨10.1109/ASPDAC.2014.6742951⟩. ⟨cea-01839860⟩