Probabilistic neuromorphic system using binary phase-change memory (PCM) synapses: Detailed power consumption analysis

D. Garbin (1) , M. Suri (1) , O. Bichler (2) , D. Querlioz (3) , C. Gamrat (2) , B. Desalvo (1)

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 IEF - Institut d'électronique fondamentale

In this paper we investigate the use of phase-change memory (PCM) devices as binary probabilistic synapses in a neuromorphic computing system for complex visual pattern extraction. Different PCM programming schemes for architectures with- or without-selector devices are provided. The functionality of the system is tested through large-scale neural network simulations. The system-level simulations show that such a system can solve a complex real-life video processing problem (vehicle counting) with high recognition rate (>94%) and low power consumption. The impact of the resistance window on the power consumption of the system is also studied. Results show that the learning-mode power consumption can be dramatically reduced if the RESET state of the PCM devices is tuned to a relatively low resistance. Read-mode power consumption, on the other hand, can be minimized by increasing the resistance values for both SET and RESET states of the PCM devices.

Domains: Engineering Sciences [physics]

D. Garbin, M. Suri, O. Bichler, D. Querlioz, C. Gamrat, et al.. Probabilistic neuromorphic system using binary phase-change memory (PCM) synapses: Detailed power consumption analysis. 2013 13th IEEE International Conference on Nanotechnology (IEEE-NANO 2013), Aug 2013, Beijing, China. pp.91-94, ⟨10.1109/NANO.2013.6721057⟩. ⟨cea-01839868⟩