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  • 1.
    Nilsson, Mattias
    et al.
    Luleå University of Technology, Sweden.
    Schelén, Olov
    Luleå University of Technology, Sweden.
    Lindgren, Anders
    RISE Research Institutes of Sweden, Digital Systems, Data Science. Luleå University of Technology, Sweden.
    Bodin, Ulf
    Luleå University of Technology, Sweden.
    Paniagua, Cristina
    Luleå University of Technology, Sweden.
    Delsing, Jerker
    Luleå University of Technology, Sweden.
    Sandin, Fredrik
    Luleå University of Technology, Sweden.
    Integration of neuromorphic AI in event-driven distributed digitized systems: Concepts and research directions2023In: Frontiers in Neuroscience, ISSN 1662-4548, E-ISSN 1662-453X, Vol. 17, article id 1074439Article in journal (Refereed)
    Abstract [en]

    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.

  • 2.
    Teeters, Jeffrey
    et al.
    University of California, USA.
    Kleyko, Denis
    RISE Research Institutes of Sweden, Digital Systems, Data Science. University of California, USA.
    Kanerva, Pentti
    University of California, USA.
    Olshausen, Bruno
    University of California, USA.
    On separating long- and short-term memories in hyperdimensional computing2023In: Frontiers in Neuroscience, ISSN 1662-4548, E-ISSN 1662-453X, Vol. 16, article id 867568Article in journal (Refereed)
    Abstract [en]

    Operations on high-dimensional, fixed-width vectors can be used to distribute information from several vectors over a single vector of the same width. For example, a set of key-value pairs can be encoded into a single vector with multiplication and addition of the corresponding key and value vectors: the keys are bound to their values with component-wise multiplication, and the key-value pairs are combined into a single superposition vector with component-wise addition. The superposition vector is, thus, a memory which can then be queried for the value of any of the keys, but the result of the query is approximate. The exact vector is retrieved from a codebook (a.k.a. item memory), which contains vectors defined in the system. To perform these operations, the item memory vectors and the superposition vector must be the same width. Increasing the capacity of the memory requires increasing the width of the superposition and item memory vectors. In this article, we demonstrate that in a regime where many (e.g., 1,000 or more) key-value pairs are stored, an associative memory which maps key vectors to value vectors requires less memory and less computing to obtain the same reliability of storage as a superposition vector. These advantages are obtained because the number of storage locations in an associate memory can be increased without increasing the width of the vectors in the item memory. An associative memory would not replace a superposition vector as a medium of storage, but could augment it, because data recalled from an associative memory could be used in algorithms that use a superposition vector. This would be analogous to how human working memory (which stores about seven items) uses information recalled from long-term memory (which is much larger than the working memory). We demonstrate the advantages of an associative memory experimentally using the storage of large finite-state automata, which could model the storage and recall of state-dependent behavior by brains. 

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