MicroQonv: Reshaping Convolution Tensors for Efficient Microscaling in Training and Inference

arXiv:2609.28358 · cs.AR, cs.AI · Submitted 2026-09-23 · Read on arXiv

cs.AR, cs.AI

Submitted: 2026-09-23

Updated: 2026-09-23

Comments: 12 pages, 7 figures

Code: https://github.com/ultralytics/ultralytics

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Quantization — 93× in this paper · explained in 12 episodes
• Accuracy — 52× in this paper · explained in 1 episode
• Continual Learning — 10× in this paper · explained in 2 episodes
• Transformer — 6× in this paper · explained in 2 episodes
• Quantized Latent Replay — 5× in this paper
• Catastrophic Forgetting — 4× in this paper · explained in 5 episodes
• Attention Mechanism — 3× in this paper · explained in 2 episodes
• Conference on Computer Vision and Pattern Recognition — 2× in this paper
• Energy Efficiency — 2× in this paper · explained in 3 episodes
• Fine-Tuning — 2× in this paper · explained in 5 episodes
• International Conference on Learning Representations — 2× in this paper
• International Symposium on Computer Architecture — 2× in this paper
• Systolic Array — 2× in this paper
• Activation Function — 1× in this paper · explained in 1 episode
• Adaptability — 1× in this paper · explained in 1 episode
• Adaptation — 1× in this paper · explained in 1 episode
• Artificial Intelligence — 1× in this paper
• Artificial Intelligence (AI) — 1× in this paper · explained in 2 episodes
• Benchmark — 1× in this paper · explained in 2 episodes
• Central Processing Unit — 1× in this paper
• Core — 1× in this paper · explained in 2 episodes
• Data Pruning — 1× in this paper · explained in 2 episodes
• Distribution Focal Loss — 1× in this paper
• Distribution Shift — 1× in this paper · explained in 5 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Pruning — 1× in this paper · explained in 4 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Scaling Laws — 1× in this paper · explained in 4 episodes
• Self-Supervised Learning — 1× in this paper · explained in 7 episodes
• Thanks to Continual Learning — 1× in this paper
• Transformer Architecture — 1× in this paper · explained in 2 episodes

Sources

• A White Paper on Neural Network Quantization
• Microscaling Data Formats for Deep Learning
• FP4 All the Way: Fully Quantized Training of LLMs
• Pretraining Large Language Models with NVFP4
• Ultralytics YOLO26: Unified Real-Time End-to-End Vision Models
• RT-DETRv4: Painlessly Furthering Real-Time Object Detection with Vision Foundation Models
• Probing Image Compression For Class-Incremental Learning

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