Going Further With Winograd Convolutions: Tap-Wise Quantization for Efficient Inference on 4x4 Tiles
Author:
Affiliation:
1. Computing Systems Lab, Huawei Zurich Research Center
Funder
Huawei Technologies
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9923754/9923780/09923839.pdf?arnumber=9923839
Cited by 6 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. Accelerating Frequency-domain Convolutional Neural Networks Inference using FPGAs;2024 IEEE International Symposium on Circuits and Systems (ISCAS);2024-05-19
2. Fast Convolution Meets Low Precision: Exploring Efficient Quantized Winograd Convolution on Modern CPUs;ACM Transactions on Architecture and Code Optimization;2024-01-19
3. Wino Vidi Vici: Conquering Numerical Instability of 8-bit Winograd Convolution for Accurate Inference Acceleration on Edge;2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV);2024-01-03
4. A 28-nm 19.9-to-258.5-TOPS/W 8b Digital Computing-in-Memory Processor With Two-Cycle Macro Featuring Winograd-Domain Convolution and Macro-Level Parallel Dual-Side Sparsity;IEEE Journal of Solid-State Circuits;2024
5. Accelerating Large Kernel Convolutions with Nested Winograd Transformation;2023 IFIP/IEEE 31st International Conference on Very Large Scale Integration (VLSI-SoC);2023-10-16
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