Title: neker
DNr: LiU-compute-2023-18
Project Type: LiU Compute
Principal Investigator: Magnus Sethson <>
Affiliation: Linköpings universitet
Duration: 2023-07-01 – 2024-07-01
Classification: 20207


neker is a first attempt for a spiking neuron network computational engine aiming for deep learning tests with a highly efficient memory footprint. Its unique feature is to project an unstructured net onto a structured computational core. This is accomplished by mixing sparse and dense matrix operations using C functions primarily from the GSL project. (neker means "sädeskärve" in old Swedish language and refers to its structured look). The main application is for evaluating condition monitoring strategies using a large set of measurements on a pneumatic test stand in our laboratory here at LiU. The project goal is to develop a small and efficient spiking neural network engine that can possible run on embedded systems. Training need super computer power though.