Research map / Architectures and efficiency / CNN design
AirWino: Optimized Winograd Convolution for Accelerating CNN Inference on ARMv8 Processors
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Closest papers by meaning
- Towards Efficient and Accurate Winograd Convolution via Full Quantization (NeurIPS 2023 · similarity 0.85)
- DWM: A Decomposable Winograd Method for Convolution Acceleration (AAAI 2020 · similarity 0.85)
- Efficient Sparse-Winograd Convolutional Neural Networks (ICLR 2018 · similarity 0.84)
- Data-Free Group-Wise Fully Quantized Winograd Convolution via Learnable Scales (CVPR 2025 · similarity 0.82)
- DEX: Data Channel Extension for Efficient CNN Inference on Tiny AI Accelerators (NeurIPS 2024 · similarity 0.81)
- FreeNet: Liberating Depth-Wise Separable Operations for Building Faster Mobile Vision Architectures (AAAI 2025 · similarity 0.81)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.
