## Paper 2: HADQ-Net: A Power-Efficient and Hardware-Adaptive Deep Convolutional Neural Network Translator...
**Authors:** Oflamaz, Yalçın
**Year:** 2025
**DOI:** [10.3390/electronics14183686](https://doi.org/10.3390/electronics14183686)

### Research Question
How can deep CNNs be deployed on resource-constrained edge devices while balancing accuracy, memory footprint, and energy consumption?

### Methodology
- **Design:** Empirical / Framework Proposal.
- **Approach:** Quantization-Aware Training (QAT) with adaptive quantization limits based on statistical properties of layers.
- **Data:** Tested on Super-resolution, Classification, Object Detection, and Semantic Segmentation tasks.

### Key Findings
1. **HADQ-Net Framework:** Introduces a pipeline that compresses FP32 weights to lower bit-widths using layer-specific adaptive limits.
2. **QAT vs. PTQ:** The authors claim **superiority of QAT over PTQ** for their specific use cases (CNNs on edge devices), contradicting the trend in massive LLMs where PTQ is preferred.
3. **Efficiency:** Demonstrates significant reductions in memory and energy consumption with maintained accuracy.

### Implications
Highlights that for "smaller" deep learning models (CNNs vs. LLMs), retraining (QAT) remains the gold standard for accuracy. It emphasizes **hardware-software co-design**, proposing a "translator" that prepares models specifically for hardware accelerators.

### Limitations
- Focuses on CNNs, which have different outlier properties than Transformers/LLMs.
- The requirement for retraining limits applicability to foundation models where training data is inaccessible or compute is limited.

### Relevance to Your Research
**Score:** ⭐⭐⭐ (3/5)
**Why:** Good counter-perspective favoring QAT, but potentially less relevant if your focus is strictly on 100B+ parameter LLMs where QAT is impossible.

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