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Hybrid Digital-Neuromorphic Computing Services: Interoperability, Interfacing and Programming

Reference number
Coordinator Luleå tekniska universitet - Luleå tekniska universitet Inst f system- & rymdteknik
Funding from Vinnova SEK 1 957 762
Project duration September 2023 - June 2024
Status Completed
Venture Emerging technology solutions
Call Emerging technology solutions stage 1 2023

Important results from the project

The project focused on the challenges associated with integrating neuromorphic information technology into the existing digital infrastructure and the edge-to-cloud continuum. Concepts for digital interfaces and abstractions were developed and examined through prototype implementations based on SNN and pub/sub/query models. The objective is to enable energy-efficient and massively distributed artificial intelligence (AI) based on the efficient and flexible use of various types of hardware from edge to cloud.

Expected long term effects

The project has developed a concept for digital interfaces to neuromorphic information technology, enabling integration with digital infrastructure in an edge-to-cloud continuum. By developing three prototypes, the project has explored the modularization of spiking neural networks, programming of neuromorphic systems, and the implementation of digital abstractions. The results contribute to skills supply and industrial renewal, with the potential for innovations that strengthen Sweden´s position in energy-efficient AI.

Approach and implementation

The project employed an iterative, agile methodology supported by two workshops organized by Vinnova. In the initial phase, the concept for digital interfaces and programming was developed based on a survey of the field and discussions with key representatives in industry and academia. Subsequently, concepts for modularization and communication were developed. Prototypes were implemented and results evaluated in project meetings. Questions and concepts have been presented at conferences and seminars.

The project description has been provided by the project members themselves and the text has not been looked at by our editors.

Last updated 9 August 2024

Reference number 2023-01363