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ARXIV:2603.09274 · NEUROMORPHIC COMPUTING · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2603.09274NEUROMORPHIC COMPUTINGSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEarXiv
DendroNN introduces a novel neural network architecture for energy-efficient classification of event-based data using dendritic mechanisms.
Opportunity summary
Pain DendroNN introduces a novel neural network architecture for energy-efficient classification of event-based data using dendritic mechanisms.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
DendroNN introduces a novel neural network architecture for energy-efficient classification of event-based data using dendritic mechanisms. Feed-forward spiking neural networks can be used to solve these tasks while offering potential benefits in terms of…
Spatiotemporal information is at the core of diverse sensory processing and computational tasks. Feed-forward spiking neural networks can be used to solve these tasks while offering potential benefits in terms of energy efficiency by…
ScienceToStartup currently rates this 4.0/10 on the public viability pass. By leveraging a DendroNN's dynamic and static sparsity along with intrinsic quantization, it achieves up to 4x higher efficiency than state-of-the-art neuromorphic hardware at…
Neuromorphic Computing moved forward this cycle; last verified April 2026. Public score 4.0/10.
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DendroNN introduces a novel neural network architecture for energy-efficient classification of event-based data using dendritic mechanisms.
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10.48550/arXiv.2603.09274DendroNN introduces a novel neural network architecture for energy-efficient classification of event-based data using dendritic mechanisms.
Abstract
Spatiotemporal information is at the core of diverse sensory processing and computational tasks. Feed-forward spiking neural networks can be used to solve these tasks while offering potential benefits in terms of energy efficiency by computing event-based. However, they have trouble decoding temporal information with high accuracy. Thus, they commonly resort to recurrence or delays to enhance their temporal computing ability which, however, bring downsides in terms of hardware-efficiency. In the brain, dendrites are computational powerhouses that just recently started to be acknowledged in such machine learning systems. In this work, we focus on a sequence detection mechanism present in branches of dendrites and translate it into a novel type of neural network by introducing a dendrocentric neural network, DendroNN. DendroNNs identify unique incoming spike sequences as spatiotemporal features. This work further introduces a rewiring phase to train the non-differentiable spike sequences without the use of gradients. During the rewiring, the network memorizes frequently occurring sequences and additionally discards those that do not contribute any discriminative information. The networks display competitive accuracies across various event-based time series datasets. We also propose an asynchronous digital hardware architecture using a time-wheel mechanism that builds on the event-driven design of DendroNNs, eliminating per-step global updates typical of delay- or recurrence-based models. By leveraging a DendroNN's dynamic and static sparsity along with intrinsic quantization, it achieves up to 4x higher efficiency than state-of-the-art neuromorphic hardware at comparable accuracy on the same audio classification task, demonstrating its suitability for spatiotemporal event-based computing. This work offers a novel approach to low-power spatiotemporal processing on event-driven hardware.
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PROBLEM
DendroNN introduces a novel neural network architecture for energy-efficient classification of event-based data using dendritic mechanisms. Feed-forward spiking neural networks can be used to solve these tasks while offering potential benefits in terms of energy efficiency by co...
METHOD
Spatiotemporal information is at the core of diverse sensory processing and computational tasks. Feed-forward spiking neural networks can be used to solve these tasks while offering potential benefits in terms of energy efficiency by computing event-based.
RESULT
ScienceToStartup currently rates this 4.0/10 on the public viability pass. By leveraging a DendroNN's dynamic and static sparsity along with intrinsic quantization, it achieves up to 4x higher efficiency than state-of-the-art neuromorphic hardware at comparable accuracy on the s...
WHY NOW
Neuromorphic Computing moved forward this cycle; last verified April 2026. Public score 4.0/10.
Abstract-backed public claims while anchored extraction refreshes.
DendroNN introduces a novel neural network architecture for energy-efficient classification of event-based data using dendritic mechanisms. Feed-forward spiking neural networks can be used to solve these tasks while offering potential benefits in terms of energy efficiency by computing event-based.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Spatiotemporal information is at the core of diverse sensory processing and computational tasks. Feed-forward spiking neural networks can be used to solve these tasks while offering potential benefits in terms of energy efficiency by computing event-based.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 4.0/10 on the public viability pass. By leveraging a DendroNN's dynamic and static sparsity along with intrinsic quantization, it achieves up to 4x higher efficiency than state-of-the-art neuromorphic hardware at comparable accuracy on the same audio classification task, demonstrating its suitability for spatiotemporal event-based computing.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Neuromorphic Computing moved forward this cycle; last verified April 2026. Public score 4.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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DendroNN introduces a novel neural network architecture for energy-efficient classification of event-based data using dendritic mechanisms.
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Neuromorphic Computing
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