Towards Temporal Information Processing – Printed Neuromorphic Circuits with Learnable Filters

With the progression of Internet of Things, many novel consumer products such as wearable devices and disposable electronics requires flexibility, biocompatibility and ultra low-costs. However, these features can hardly be matched by traditional silicon-based electronics. In this regard, printed electronics becomes one of the most competitive candidate by offering the aforementioned properties thanks to its additive manufacturing process. To address fundamental signal-processing tasks, printed neuromorphic circuits, emulating the artificial neural networks, have received increasing attention, as they can achieve appealing computational capabilities by assembling simple elemental circuit primitives. However, many target applications for printed electronics are based on processing temporal sensory data, which is beyond the reach of existing printed neuromorphic circuits, since they lack components with time dependencies. To this end, this paper proposes a novel printed temporal processing block that combines existing circuit primitives with a sequence of learnable low-pass filters. We model the proposed circuit and proposed the corresponding training objective to enable the bespoke design of the circuits. ... mehrSimulations on 15 benchmark time-series datasets reveal that, in comparison to existing printed neuromorphic circuits, the proposed circuits can effectively process temporal information by using 1.5× and 1.3× of device counts and power respectively. Moreover, the achieved classification accuracy reaches 98% of that from classic hardware-agnostic Elman recurrent neural networks.