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Nilsson M, Schelén O, Lindgren A, Bodin U, Paniagua C, Delsing J, Sandin F. Integration of neuromorphic AI in event-driven distributed digitized systems: Concepts and research directions. Front Neurosci 2023; 17:1074439. [PMID: 36875653 PMCID: PMC9981939 DOI: 10.3389/fnins.2023.1074439] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Accepted: 01/23/2023] [Indexed: 02/19/2023] Open
Abstract
Increasing complexity and data-generation rates in cyber-physical systems and the industrial Internet of things are calling for a corresponding increase in AI capabilities at the resource-constrained edges of the Internet. Meanwhile, the resource requirements of digital computing and deep learning are growing exponentially, in an unsustainable manner. One possible way to bridge this gap is the adoption of resource-efficient brain-inspired "neuromorphic" processing and sensing devices, which use event-driven, asynchronous, dynamic neurosynaptic elements with colocated memory for distributed processing and machine learning. However, since neuromorphic systems are fundamentally different from conventional von Neumann computers and clock-driven sensor systems, several challenges are posed to large-scale adoption and integration of neuromorphic devices into the existing distributed digital-computational infrastructure. Here, we describe the current landscape of neuromorphic computing, focusing on characteristics that pose integration challenges. Based on this analysis, we propose a microservice-based conceptual framework for neuromorphic systems integration, consisting of a neuromorphic-system proxy, which would provide virtualization and communication capabilities required in distributed systems of systems, in combination with a declarative programming approach offering engineering-process abstraction. We also present concepts that could serve as a basis for the realization of this framework, and identify directions for further research required to enable large-scale system integration of neuromorphic devices.
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Affiliation(s)
- Mattias Nilsson
- Embedded Intelligent Systems Lab (EISLAB), Department of Computer Science, Electrical and Space Engineering, Luleå University of Technology, Lulea, Sweden
| | - Olov Schelén
- Embedded Intelligent Systems Lab (EISLAB), Department of Computer Science, Electrical and Space Engineering, Luleå University of Technology, Lulea, Sweden
| | - Anders Lindgren
- Embedded Intelligent Systems Lab (EISLAB), Department of Computer Science, Electrical and Space Engineering, Luleå University of Technology, Lulea, Sweden.,Applied AI and IoT, Industrial Systems, Digital Systems, RISE Research Institutes of Sweden, Kista, Sweden
| | - Ulf Bodin
- Embedded Intelligent Systems Lab (EISLAB), Department of Computer Science, Electrical and Space Engineering, Luleå University of Technology, Lulea, Sweden
| | - Cristina Paniagua
- Embedded Intelligent Systems Lab (EISLAB), Department of Computer Science, Electrical and Space Engineering, Luleå University of Technology, Lulea, Sweden
| | - Jerker Delsing
- Embedded Intelligent Systems Lab (EISLAB), Department of Computer Science, Electrical and Space Engineering, Luleå University of Technology, Lulea, Sweden
| | - Fredrik Sandin
- Embedded Intelligent Systems Lab (EISLAB), Department of Computer Science, Electrical and Space Engineering, Luleå University of Technology, Lulea, Sweden
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