寒武纪论文对比记录

重点论文：

   寒武纪从2014年开始：
（1）DianNao: A Small-Footprint High-Throughput Accelerator for Ubiquitous Machine-Learning
（2）DaDianNao: A Machine-Learning Supercomputer
（3）PuDianNao: A Polyvalent Machine Learning Accelerator
（4）ShiDianNao: Shifting Vision Processing Closer to the Sensor
（5）Cambricon-X: An Accelerator for Sparse Neural Networks

论文侧重点：

（1）DianNao：可以看作是硬件设计的基础

（2）DaDianNao：面向服务器端的高性能计算架构

（3)  ShiDianNao：面向边缘端设备应用场景的

   (4)  PuDianNao：面向更加泛化的机器学习算法的

（5）combricon：面向更加广泛的机器学习加速器的指令集架构。

寒武纪的DianNao系列芯片构架也采用了流式处理的乘加树（DianNao[2]、DaDianNao[3]、PuDianNao[4]）和类脉动阵列的结构（ShiDianNao[5]）。为了兼容小规模的矩阵运算并保持较高的利用率，同时更好的支持并发的多任务，DaDianNao和PuDianNao降低了计算粒度，采用了双层细分的运算架构，即在顶层的PE阵列中，每个PE由更小规模的多个运算单元构成，更细致的任务分配和调度虽然占用了额外的逻辑，但有利于保证每个运算单元的计算效率并控制功耗，

1. DianNao

    参考：https://blog.csdn.net/evolone/article/details/80765094

2. DaDianNao

 参考：https://blog.csdn.net/u013108511/article/details/88831132

3. ShiDianNao

参考：https://blog.csdn.net/evolone/article/details/82594250

https://www.dazhuanlan.com/2019/12/18/5df9db0b0812e/

https://blog.csdn.net/weixin_33810006/article/details/87977439

          

 

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