RCENet: Recursive Concatenation and Enhancement Network for Real-Time Super-Resolution
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Recent advancements in edge AI have increased demand for real-time vision models that run efficiently on edge devices. However, their architectural heterogeneity in terms of compute structure, memory bandwidth, and supported tasks requires a vision model optimized for each edge device. Therefore, we present the Recursive Concatenation and Enhancement Network (RCENet), a lightweight and efficient Single Image Super-Resolution (SISR) model optimized for Google Tensor Processing Units (TPUs). To optimize the architecture for Google TPUs, we first conduct a detailed analysis of computational characteristics and runtime behavior to inform the network design. As a result, RCENet leverages hardware-efficient operators and quantization-friendly modules. We further propose Operator-Selective Quantization (OSQ) combined with Quantization-Aware Distillation (QAD), tailored to the TPU architecture, to enable deployment on integer-only inference engines without compromising perceptual quality. Extensive experiments on standard benchmarks show that RCENet delivers competitive visual quality with significantly reduced latency and power consumption. In particular, RCENet achieves more than 70 FPS on Google TPUs, while maintaining visual quality comparable to that of much more complex models. Our method achieved second place on the Quantized Super-Resolution track of the 2025 Mo-bileAI (MAl) Challenge, demonstrating its effectiveness for real-world deployment. Our project page is available at https://rlghksdbs.github.ioIRCENet/
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it