As of digital processing engines, the partners propose to develop plastic Neural Networks (NNs) customised for specific applications and capable of operating in extremely parallel fashion to achieve high performance at low power. With this project, both ARM and PragmatIC hope to establish digital hardware NNs as the de-facto processing engine for printed electronics.
"NNs are particularly interesting for sensing applications in the real world, with a combination of different sensor inputs. They are good at classifying the data so it can be interpreted according to the categories of results you are looking for" commented White about the PlasticARMPit project.
"What's more, the physical structure of the flexible ICs translates well into neural networks. The comparative work performance and yield of plastic electronics is less of an issue in neural networks where there is a lot of redundancy" added White, saying that imprinted electronics on plastic foils allows for the modelling of large number of neurons. "Building circuits layer by layer, we can physically build something that mimics a neural network architecture. It is not for high-end machine learning of course, but for smart packaging and sensing applications, you can get a form of categorization from thin flexible circuits".
In the case of the PlasticARMPit project, Unilever was the one who came with the business case. The idea is to couple a flexible and multi-analyte e-Nose sensor with a plastic NN onto a wearable patch to detect armpit malodour composition and determine how effective the company's antiperspirants and deodorants are. In this particular research project running until March 2020, PragmatIC will integrate organic TFT bio-sensors developed at the University of Manchester, but outside of this project, it could take sensors from different partners.
"Our focus is on interpreting the analogue inputs and convert it to useful data. Sensing companies interested in the computing backend could come to us", said the CEO.