SYMONE Project: Synaptic Molecular Networks for Bio-Inspired Information Processing

Brain-inspired approaches emphasize the need for highly connected complex networks with long-range adaptive connections (the distant synapses). If implemented with non-biological technologies, these are raising problems with respect to: charging/discharging, cross-talk, delays, losses and heating, i.e. scalability issues well-known from CMOS technologies. Instead, SYMONE will explore the functionalities of bio-inspired scalable near-neighbour (locally-connected) networks and systolic-like array architectures. The SYMONE long-term vision is to build multi-scale bio-/neuro-inspired systems interfacing/connecting molecular-scale devices to macroscopic systems for unconventional information processing with scalable neuromorphic architectures. The SYMONE computational substrate is a memristive/synaptic network controlled by a multi-terminal structure of input/output ports and internal gates embedded in a classical digital CMOS environment. The SYMONE goal is the exploration of a multiscale platform connecting molecular-scale devices into networks for the development and testing of synaptic devices and scalable neuromorphic architectures, and for investigating materials and components with new functionalities. The generic breakthrough concerns proof-of-concept of unconventional information processing involving flow of information via near-neighbour short-range (local) interactions through a network of non-linear elements: switches, memristors/synapses. These will require several breakthroughs concerning the functionality of reasonably complex networks of simple components, and the fabrication of networks of devices, including self-assembly and multi-scale interfacing/contacting between such networks

Aspects of computing with locally connected networks

This paper outlines an ongoing effort to develop and use networks of nanoscalememristivecomponents for unconventional information processing

Aspects of computing with locally connected networks

This paper outlines an ongoing effort to develop and use networks of nanoscalememristivecomponents for unconventional information processing

Variability-tolerant Convolutional Neural Network for Pattern Recognition applications based on OxRAM synapses

International audienceSoftware implementations of artificial Convolutional Neural Networks (CNNs), taking inspiration from biology, are at the state-of-the-art for Pattern Recognition (PR) applications and they are successfully used in commercial products [1]. However, they require power-hungry CPU/GPU to perform convolution operations based on computationally expensive sums of multiplications. This hinders their integration in portable devices. Some full CMOS-based hardware implementations of CNN have been suggested, but they still require the computation of multiplications [2]. In this work, we present for the first time to our knowledge a spike-based hardware implementation of CNN using HfO2 based OxRAM devices as binary synapses. OxRAM devices are chosen for their low switching energy [3] and promising endurance performance [4]. We perform an experimental and theoretical study of the impact of programming conditions at both device and system levels. A complex visual pattern recognition application is demonstrated with a spike-based hierarchical CNN, inspired from the mammalian visual cortex organization. A high accuracy (pattern recognition rate >94%) is obtained for all the tested programming conditions, even if the variability associated to weaker programming conditions is larger

CBRAM devices as binary synapses for low-power stochastic neuromorphic systems: auditory (cochlea) and visual (retina) cognitive processing applications

In this work, we demonstrate an original methodology to use Conductive-Bridge RAM (CBRAM) devices as binary synapses in low-power stochastic neuromorphic systems. A new circuit architecture, programming strategy and probabilistic STDP learning rule are proposed. We show, for the first time, how the intrinsic CBRAM device switching probability at ultra-low power can be exploited to implement probabilistic learning rule. Two complex applications are demonstrated: real-time auditory (from 64-channel human cochlea) and visual (from mammalian visual cortex) pattern extraction. A high accuracy (audio pattern sensitivity >2, video detection rate >95%) and ultra-low synaptic-power dissipation (audio 0.55μW, video 74.2μW) are obtained

Carbon nanotube programmable devices for adaptive architectures

International audienc