Invited Speakers

Optoelectronic memories for retina-inspired machine vision

Abstract

The number of nodes typically used in sensory networks is growing rapidly, leading to large amounts of redundant data being exchanged between sensory terminals and computing units. To efficiently process such large amounts of data, and decrease power consumption, it is necessary to develop approaches to computing that operate close to or inside sensory networks, and that can reduce the redundant data movement between sensing and processing units. the complex circuitry of artificial visual systems based on conventional image sensors, memory and processing units presents serious challenges in terms of device integration and power consumption. Here we show simple two-terminal optoelectronic resistive random access memory (ORRAM) synaptic devices for an efficient neuromorphic visual system that exhibit non-volatile optical resistive switching and light-tunable synaptic behaviours. We examine the concept of near-sensor and in-sensor computing, in which computation tasks are moved partly to the sensory terminals. We classify functions into low-level and high-level processing, and discuss implementation of near-sensor and in-sensor computing for different physical sensing systems. We also show the in-sensor visual adaptation for accurate image perception.

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