塞内卡:构建一个完全数字的神经形态处理器,设计的权衡和挑战
Guangzhi Tang1, Kanishkan Vadivel1, Yingfu Xu1
1Imec, Eindhoven, Netherlands.
Frontiers in neuroscience
|July 10, 2023
概括
这项研究介绍了SENECA,一种灵活的神经形态处理器架构. 通过一种新的层次控制系统,SENECA提高了神经网络算法的能源和区域效率.
科学领域:
- 计算机工程 计算机工程
- 人工智能的人工智能
- 神经科学是一个神经科学.
背景情况:
- 神经形态处理器模拟大脑原理,以实现高效,低功耗的计算.
- 现有的设计往往缺乏灵活性,导致各种神经网络算法的性能和内存低效.
研究的目的:
- 提出SENECA,一个平衡灵活性和效率的数字神经形态架构.
- 展示SENECA对各种神经网络,设备内学习和前后处理的高效映射的能力.
主要方法:
- 设计为SENECA,具有具有灵活RISC-V控制器和优化的Loop Buffer控制器的层次控制系统.
- 实现了一个灵活的计算管道,用于各种算法部署.
- 为了可扩展的架构,利用了芯片上的网络.
主要成果:
- 与现有设计相比,SENECA显示了更好的能源和面积效率.
- 一个SENECA核心占地0.47mm2 (GF-22nm) 并且每次突触操作消耗2.8 pJ.
- 实验结果验证了各种算法的高效映射,并突出了设计的权衡.
结论:
- 塞内卡提供了一种高效且可编程的数字神经形态处理器.
- 该架构有效地解决了神经形态计算中的灵活性-效率权衡.
- 对于学术研究而言,SENECA平台是可用的.
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