Abstract
State-of-the-art IoT technologies request novel design solutions in edge computing, resulting in even more portable and energy-efficient hardware for in-the-field processing tasks. Vision sensors, processors, and hardware accelerators are among the most demanding IoT applications. Resistance switching (RS) two-terminal devices are suitable for resistive RAMs (RRAM), a promising technology to realize storage class memories. Furthermore, due to their memristive nature, RRAMs are appropriate candidates for in-memory computing architectures. Recently, we demonstrated a CMOS compatible silicon nitride (SiNx) MIS RS device with memristive properties. In this paper, a report on a new photodiode-based vision sensor architecture with in-memory computing capability, relying on memristive device, is disclosed. In this context, the resistance switching dynamics of our memristive device were measured and a data-fitted behavioral model was extracted. SPICE simulations were made highlighting the in-memory computing capabilities of the proposed photodiode-one memristor pixel vision sensor. Finally, an integration and manufacturing perspective was discussed.
Highlights
During the last decade, it became apparent that created data are increasing rapidly, requesting revolutionary solutions when memory and storage is concerned
The obvious step of utilizing a more specific-oriented CMOS-based design can be significantly enhanced with the addition of novel nanoelectronic devices with memory abilities, namely memristors, to be combined with the Internet of Things (IoT) sensors
To further investigate the promising aspects of such a hardware approach enabling in-memory computation at IoT sensors, special interest is on vision sensors as a fine candidate for edge computing
Summary
It became apparent that created data are increasing rapidly, requesting revolutionary solutions when memory and storage is concerned. The most straightforward approach to tackle the uprising urgent issue is the local pre-processing of the unstructured data generated by the IoT sensors in an edge-based sense [1,2,3,4] Such a promising solution will eventually minimize the requesting power consumption of the corresponding IoT applications and at the same time advance the overall computing in terms of energy efficiency. The proposed Xbar design and integrations of memristors with photodiodes for image sensing and in-memory processing, alike edge computing, sounds promising, and the presented SPICE-based simulation results reveal its successful implementation. 28 × 28 1D1M Xbar circuit array SPICE simulation results exploit the in-memory processing abilities of the proposed vision sensor
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