MAKAN: Memristive Accelerated Kolmugruv-Arnold Networks
Bibliographic record
Abstract
Traditional computing architectures struggle to keep up with modern neural networks due to energy consumption and processing speed limitations. To address these challenges, we propose MAKAN, a framework that integrates Kolmogorov-Arnold Networks (KANs) with the BITLITE memristor-based computing platform. KANs, which use learnable activation functions on edges instead of fixed functions on nodes, offer superior flexibility and accuracy, but their reliance on spline-based functions limits scalability. MAKAN overcomes this by using hat functions for piecewise linear (PWL) function approximation, efficiently implemented on BITLITE's bit-wise memristor crossbars for matrix-vector multiplication (MVM). By employing machine learning to reconstruct KAN's spline functions with PWL on memristor circuits, MAKAN significantly reduces computational overhead while preserving accuracy and interpretability. This integration results in notable improvements in energy efficiency and processing speed, making advanced neural networks more scalable and practical for real-world applications, paving the way for future advancements in energy-efficient machine learning systems.
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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 | 0.001 |
| 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 | 0.001 | 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".