Dopant network processing units as tuneable extreme learning machines

Inspired by the highly efficient information processing of the brain, which is based on the chemistry and physics of biological tissue, any material system and its physical properties could in principle be exploited for computation. However, it is not always obvious how to use a material system's computational potential to the fullest. Here, we operate a dopant network processing unit (DNPU) as a tuneable extreme learning machine (ELM) and combine the principles of artificial evolution and ELM to optimise its computational performance on a non-linear classification benchmark task. We find that, for this task, there is an optimal, hybrid operation mode (tuneable ELM mode) in between the traditional ELM computing regime with a fixed DNPU and linearly weighted outputs (fixed-ELM mode) and the regime where the outputs of the non-linear system are directly tuned to generate the desired output (direct-output mode). We show that the tuneable ELM mode reduces the number of parameters needed to perform a formant-based vowel recognition benchmark task. Our results emphasise the power of analog in-matter computing and underline the importance of designing specialised material systems to optimally utilise their physical properties for computation.

Automated summary

Inspired by the brain's efficient information processing, any material system can be used for computation, but maximizing its computational potential is not always obvious. In this study, a dopant network processing unit (DNPU) is operated as a tuneable extreme learning machine (ELM) to optimize its performance on a non-linear classification task. The results show that a hybrid operation mode, called tuneable ELM mode, reduces the parameters needed for a vowel recognition benchmark task. This highlights the power of analog in-matter computing and the importance of designing specialized material systems for computation.