DOI: https://doi.org/10.29363/nanoge.matnec.2022.004
Publication date: 23rd February 2022
Mixed-signal neuromorphic processors with dynamic neurosynaptic elements and sparse, event-based information coding are used for ultra-low-power inference and learning. In such processors, the dynamic properties of the neurosynaptic elements are inhomogeneous due to device mismatch, i.e., the dissimilarity between small-scale integrated circuit (IC) components. We describe how the resulting inhomogeneous neurosynaptic dynamics have been exploited for resource-efficient spatiotemporal inference with single neurons and spiking neural networks. For example, we demonstrate: (i) how inhibitory–excitatory pairs of dynamic synapses can be used to mimic non-spiking post inhibitory rebound in an auditory feature detection circuit of crickets, and (ii) how spatiotemporal receptive fields of output-neurons in the Spatiotemporal Correlator (STC) network can be implemented using balanced disynaptic inputs instead of dedicated axonal or neuronal delays. In experiments with the DYNAP-SE neuromorphic processor, we demonstrate synapse level temporal feature tuning with a configurable timescale of up to 0.1 seconds. This approach leads to about one order of magnitude reduction of the energy dissipation per lateral connection in an STC-type network with disynaptic dynamics instead of dedicated delay neurons. We discuss some desired properties of neuromorphic devices and materials in a regime where device mismatch constitutes the basis for efficient inference and reproducible learning.
This work was supported by The Kempe Foundations, project numbers JCK-1809 and SMK-1429.
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