可扩展的多FPGA HPC 架构用于关联记忆系统
IEEE transactions on biomedical circuits and systems
|August 20, 2024
概括
本研究介绍了贝叶斯信任传播神经网络 (BCPNNs) 的新型多FPGA架构,增强关联记忆性能. 与复杂的认知任务的GPU模拟相比,基于FPGA的系统在延迟和功率效率方面提供了显著的改进.
科学领域:
- 计算神经科学是一种神经科学.
- 硬件加速器 硬件加速器
- 人工智能的人工智能
背景情况:
- 关联记忆对于人类的认知智能至关重要.
- 贝叶斯信任传播神经网络 (BCPNNs) 有效地模拟关联记忆,但在GPU上面临可扩展性挑战.
- 当前基于GPU的BCPNN模拟显示,随着模型大小的增加,延迟和功率效率受到限制.
研究的目的:
- 为基于BCPNN的关联存储器提出一个可扩展的多FPGA高性能计算 (HPC) 架构.
- 为了解决大型BCPNN模型的GPU模拟中遇到的延迟和功率效率问题.
- 在关联记忆任务中,优化架构的空间和时间可扩展性.
主要方法:
- 开发了一个多FPGA架构,集成超列单元 (HCU) 核心,用于在线学习和推理.
- 实现了基于尖峰的同步方案,用于FPGA之间的通信.
- 采用基于人口的模型映射,基于数据包的尖端同步和基于集群的时间优化,用于多FPGA实现.
主要成果:
- 验证了两个Xilinx Alveo U50 FPGA卡的架构,支持高达22010个神经元在220 MHz.
- 实现了两个FPGA实现的268.82的最大规模延迟比 (SLR).
- 与双GPU系统相比显示了显著的改进,延迟减少了51.72%,功率减少超过5.28倍.
结论:
- 拟议的多FPGA架构为基于BCPNN的关联记忆提供了一个可扩展和高效的解决方案.
- 与GPU同行相比,该系统在延迟,功耗和模式存储容量方面表现出卓越的性能.
- 这种硬件加速方法为更具生物学可信性和高效的神经形态计算系统铺平了道路.
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