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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(More)
In this paper, we present an alternative approach to neuromorphic systems based on multi-level resistive memory (RRAM) synapses and deterministic learning rules. We demonstrate an original methodology to use conductive-bridge RAM (CBRAM) devices as, easy to program and low-power, binary synapses with stochastic learning rules. New circuit architecture,(More)
Neuromorphic architectures that exploit emerging resistive memory devices as synapses are currently receiving a lot of interest. Phase Change Memory (PCM), in particular, is a strong candidate for such architectures. However, it suffers from a resistance-drift effect in the amorphous phase (high-resistance). In this work, we investigate the impact of(More)
We present an original methodology to design hybrid neuron circuits (CMOS + non volatile resistive memory) with stochastic firing behaviour. In order to implement stochastic firing, we exploit unavoidable intrinsic variability occurring in emerging non-volatile resistive memory technologies. In particular, we use the variability on the ‘time-to-set’ (tset)(More)
In this work, we will focus on the use of Phase Change Memory (PCM) to emulate synaptic behavior in emerging neuromorphic system-architectures. In particular, we will originally show that the performance and energyefficiency of large scale neuromorphic systems can be improved by engineering individual PCM devices used as synapses. This is obtained by adding(More)
In this paper, we show that Phase Change Memory (PCM) can be used to emulate specific functions of a biological synapse similar to Long Term Potentiation (LTP) and Long Term Depression (LTD) plasticity effects. The dependence of synaptic weight on programming pulse width and pulse amplitude is shown experimentally for the PCM devices. Different combinations(More)
In this paper we present a multimodal authentication (person identification) system based on simultaneous recognition of face and speech data using a novel bio-inspired architecture powered by the CM1K chip. The CM1K chip has a constant recognition time irrespective of the size of the knowledge base, which gives massive time gains in learning and(More)
In this paper, we show how metal-oxide (OxRAM) based nanoscale memory devices can be exploited to design low-power Extreme Learning Machine (ELM) architectures. In particular we fabricated HfO<sub>2</sub> and TiO<sub>2</sub> based OxRAM devices, and exploited their intrinsic resistance spread characteristics to realize ELM hidden layer weights and neuron(More)
This tutorial is aimed to present an overview on the latest research thrusts in the field of Neuromorphic Computing. We will discuss some of the fundamental principles of computation employed by the brain, and algorithmic ideas that are currently being explored for various applications. We will then discuss new ideas that are being explored to mimic the(More)