基于FET的Ferroelectric上下文切换FPGA,使适应式深度学习机器的动态重新配置成为可能.
Yixin Xu1, Zijian Zhao2, Yi Xiao1
1Pennsylvania State University, State College, PA 16802, USA.
Science advances
|January 17, 2024
概括
本研究介绍了一种基于铁电场效应晶体管 (FeFET) 的新型FPGA,可实现动态重新配置. 这项创新克服了传统的权衡,大大减少了深度学习加速的面积和功耗.
科学领域:
- 集成电路 集成电路
- 计算机架构 计算机架构
- 材料科学 材料科学 材料科学
背景情况:
- 现场可编程门数组 (FPGA) 对于加速深度学习至关重要,因为它们的灵活性.
- 传统的FPGA在平衡芯片面积和重新配置延迟方面面临限制.
- 对于复杂的应用程序来说,高效的动态重新配置仍然是一个挑战.
研究的目的:
- 提出并验证使用铁电场效应晶体管 (FeFET) 的新型FPGA架构.
- 为了实现高效的上下文切换和动态重新配置,而不会中断活跃的操作.
- 为了克服传统FPGA设计中固有的权衡.
主要方法:
- 为FPGA面料 (查看表和连接块) 开发基于FeFET的原始程序.
- 对拟议的紧型FPGA原体进行实验验证.
- 评估面积,功耗,关键路径延迟和节省时间.
主要成果:
- 查看表 (LUT) 面积 (63.0%) 和连接块 (CB) 面积 (74.7%) 显著减少.
- 在CB (82.7%) 和开关盒 (53.6%) 的功耗大幅下降.
- 最小的关键路径延迟处罚 (9.6%) 和相当大的时间节省用于上下文切换 (78.7%) 和动态重新配置 (20.3%).
结论:
- 基于FeFET的FPGA为克服传统设计局限性提供了可行的解决方案.
- 拟议的架构在面积,功率效率和重新配置速度方面取得了显著的改进.
- 这项技术为深度学习和其他应用程序中更有效和更动态的硬件加速铺平了道路.
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