Skip to Main content Skip to Navigation
Journal articles

1S1R optimization for high‐frequency inference on binarized spiking neural networks

Abstract : Single memristor crossbar arrays are a very promising approach to reduce the power consumption of deep learning accelerators. In parallel, the emerging bio-inspired Spiking Neural Networks (SNNs) offer very low power consumption with satisfactory performance on complex artificial intelligence tasks. In such neural networks, synaptic weights can be stored in non-volatile memories. These latter are massively read during inference, which can lead to device failure. In this context, we propose a 1S1R (1 Selector 1 Resistor) device composed by a HfO2-based OxRAM memory stacked on a Ge-Se-Sb-N-based Ovonic Threshold Switch (OTS) back-end selector for high-density Binarized SNNs (BSNNs) synaptic weight hardware implementation. An extensive experimental statistical study combined with a novel Monte Carlo model allows to deeply analyze the OTS switching dynamics based on field-driven stochastic nucleation of conductive dots in the layer. This allows quantifying the occurrence frequency of OTS erratic switching as function of the applied voltages and 1S1R reading frequency. The associated 1S1R reading error rate is calculated. Focusing on the standard machine learning MNIST image recognition task, BSNN figures of merit (footprint, electrical consumption during inference, frequency of inference, accuracy, and tolerance to errors) are optimized by engineering the network topology, training procedure, and activations sparsity.
Complete list of metadata
Contributor : Joel MINGUET LOPEZ Connect in order to contact the contributor
Submitted on : Tuesday, June 28, 2022 - 3:41:01 PM
Last modification on : Saturday, September 24, 2022 - 2:58:04 PM


Adv Elect Materials - 2022 - M...
Files produced by the author(s)



Joel Minguet Lopez, Quentin Rafhay, Manon Dampfhoffer, Lucas Reganaz, Niccolo Castellani, et al.. 1S1R optimization for high‐frequency inference on binarized spiking neural networks. Advanced Electronic Materials, Wiley, 2022, 2022, pp.2200323. ⟨10.1002/aelm.202200323⟩. ⟨cea-03707409⟩



Record views


Files downloads