Spike based processing chain for signal classification
Published : 11 June 2019
The expansion of the internet of things is conditioned by our ability to develop innovative systems able to apprehend and understand the environment while having an ultra low power consumption, compatible with energy harvesting.
To reach such a goal, one of the solution which is knowing a considerable renewed interest is the use of acoustic signals. Their low frequencies undoubtedly induces a low power consumption in the circuit interface and their low cost eases the dissemination of this solution. There’s a huge applicative potential: wake-up by key words (the well known “ok google”), choc detection, source localization, event classification, surveillance, and machine health monitoring.
In order to implement such complex functions in an energy efficient manner, the potential of neural networks is more and more considered. However today, these solutions are too power consuming. To reduce this power, several alternatives are considered. One of the most promising is the coding of the signal in spike, coherently with neuromorphic architecture. Recently, CEA-LETI has developed a new ADC architecture which directly generate some spike and the best power efficiency in the state of the art has been reached. The aim of this PhD is to follow up this work by implementing in the analog domain some feature extraction in order to reduce the complexity of the neural network processing. To reach the best energy efficiency, a joint optimization between the analog, digital and algorithmic part is mandatory.
In the scope of this PhD, CEA-LETI and EPDFL are collaborating to develop this new analog processing interface, adapted to neural networks based on spike processing. The main objective is ti setup a methodology to reduce the power consumption in all the sensing systems. The automotive applications will be particularly considered. Other application areas and different kind of signal might be also studied.