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# AI电路与系统
序号 | 论文题目 | 期刊名卷期号 | 推荐理由 | 对我们的意义 | 推荐人 | 下载地址 |
----- | ----- | ----- | ----- | ----- | ----- | ----- |
1 | AdderNet: Do We Really Need Multiplications in Deep Learning? | Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2020 | Addernet最初提出的论文，后续的一系列工作都是基于该论文 | 我们后续做的无乘法网络的idea都是基于该论文，做AI电路的同学必读 | 孙彪 | [点此下载](https://openaccess.thecvf.com/content_CVPR_2020/html/Chen_AdderNet_Do_We_Really_Need_Multiplications_in_Deep_Learning_CVPR_2020_paper.html)
1 | AdderNet: Do We Really Need Multiplications in Deep Learning? | Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2020 | Addernet最初提出的论文，后续的一系列工作都是基于该论文 | 我们后续做的无乘法网络的idea都是基于该论文，做AI电路的同学必读 | 孙彪 | [点此下载](https://openaccess.thecvf.com/content_CVPR_2020/html/Chen_AdderNet_Do_We_Really_Need_Multiplications_in_Deep_Learning_CVPR_2020_paper.html) |
2 | Conjugate Adder Net (CAddNet) - A Space-Efficient Approximate CNN | Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2022 | 另外一种无乘法网络CAdder | 做AI电路的同学必读 | 孙彪 | [点此下载](https://openaccess.thecvf.com/content/CVPR2022W/ECV/html/Shen_Conjugate_Adder_Net_CAddNet_-_A_Space-Efficient_Approximate_CNN_CVPRW_2022_paper.html) |
3 | Redistribution of Weights and Activations for AdderNet Quantization | Advances in Neural Information Processing Systems 35 (2022): 22739-22751 | 华为做Addernet量化的文章，号称可以量化到4bit | 做AI电路的同学必读 | 孙彪 | [点此下载](https://proceedings.neurips.cc/paper_files/paper/2022/hash/8f15e0b418ccdefec8313affc897dc8c-Abstract-Conference.html) |
4 | WSQ-AdderNet: Efficient Weight Standardization Based Quantized AdderNet FPGA Accelerator Design with High-Density INT8 DSP-LUT Co-Packing Optimization | Proceedings of the 41st IEEE/ACM International Conference on Computer-Aided Design. 2022 | 我们团队发表的Addernet量化文章，可以量化到8bit | 做AI电路的同学必读 | 孙彪 | [点此下载](https://dl.acm.org/doi/abs/10.1145/3508352.3549439) |
5 | Adder Attention for Vision Transformer | Advances in Neural Information Processing Systems 34 (2021): 19899-19909 | 华为将Adder应用到Vision Transformer上的文章 | 做AI电路的同学必读 | 孙彪 | [点此下载](https://proceedings.neurips.cc/paper/2021/hash/a57e8915461b83adefb011530b711704-Abstract.html) |
6 | Searching for Energy-Efficient Hybrid Adder-Convolution Neural Networks | Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2022 | Adder和CNN结合用NAS搜索混合网络结构 | 对于我们做NAS方法有一定启发，可以作为baseline | 孙彪 | [点此下载](https://openaccess.thecvf.com/content/CVPR2022W/NAS/html/Li_Searching_for_Energy-Efficient_Hybrid_Adder-Convolution_Neural_Networks_CVPRW_2022_paper.html) |