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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.