A real-valued DCT-based spectral CNN architecture for efficient edge deep learning
Bibliographic record
Abstract
Spectral Convolutional Neural Networks (SpCNNs) offer a pathway to computational efficiency by performing convolutions in the frequency domain. While FFT-based SpCNNs reduce convolutional complexity, their reliance on complex valued operations and inverse transforms incurs high memory and latency costs, limiting their utility for embedded and edge applications. This paper proposes an enhanced, real-valued Discrete Cosine Transform (DCT)-based SpCNN that eliminates inverse transforms and complex arithmetic by performing all operations, including convolution, activation, and pooling entirely in the DCT domain. A modified real-valued spectral activation function is introduced to enable effective nonlinearity in frequency space. Experimental evaluation on MNIST and a 94-class ASCII benchmark datasets demonstrates that the proposed model achieves up to 20% reduction in computational workload and 19% reduction in memory access compared to a previous spectral model. Additionally, LeNet5 achieves improved accuracy (98.44%), and the architecture exhibits significantly faster inference and higher energy efficiency in both batch and real time settings. These results establish the DCT-based SpCNN as a practical and scalable solution for deployment in resource constrained systems. • Real-valued DCT-based SpCNN with no inverse transforms or complex numbers. • DCT-domain spectral activation enables full-frequency nonlinear operations. • Reduces FLOPs by 20% and memory cost by 19% versus prior spectral models. • Achieves 98.44% accuracy on MNIST and strong results on ASCII-94 dataset. • Evaluated for latency, power, and throughput in batch and real-time modes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".