Hybrid Neuromorphic-Deep Learning Systems for AI Acceleration in Edge Computing
Bibliographic record
Abstract
The growing demand for energy-efficient and responsive artificial intelligence (AI) systems at the edge has intensified interest in neuromorphic computing, which mimics the brain’s mechanisms to enable low-power, real-time data processing. While neuromorphic systems excel in energy efficiency, their scalability and broader applicability remain constrained. To address these limitations, this study introduces a hybrid framework that combines spiking neural networks (SNNs) with conventional deep learning architectures such as convolutional neural networks (CNNs). By leveraging the strengths of both paradigms, the proposed system enhances AI acceleration for edge computing environments characterized by resource constraints. A detailed mathematical representation of the hybrid system is developed, followed by performanceevaluations using established datasets. The results highlight significant gains in energy efficiency, achieving reductions of up to 35%, alongside latency improvements of up to 45% compared to existing neuromorphic and traditional AI methods. Moreover, the system demonstrates scalability and adaptability to diverse edge applications, including Internet of Things (IoT) devices and autonomous systems. These findings underline the transformative potential of hybrid neuromorphic-deep learning architectures in advancing the capabilities of next-generation edge AI while bridging the gap between bioinspired and conventional computational methods.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".