SPIRE:一个28纳米内存高效的多储LSM加速器,用于适应性和灵活的时间序列分类.
IEEE transactions on biomedical circuits and systems
|February 25, 2026
概括
本研究介绍了SPIRE,这是一款用于高效时间序列分类的新型数字多储液态机器 (LSM). 在边缘计算应用中,SPIRE显著提高了突触密度,并减少了边缘计算应用的内存足迹.
科学领域:
- 神经形态工程的神经形态工程
- 人工智能的人工智能
背景情况:
- 尖端神经网络 (SNN) 在序列数据方面表现出色,但在长期依赖性方面扎.
- 液态机器 (LSM) 通过分离反复性和分类来提供解决方案,但硬件实现面临内存和性能权衡.
研究的目的:
- 开发一个紧的,内存高效的,高性能的多储LSM硬件.
- 解决现有LSM实施的时间序列分类和边缘部署的设计限制.
主要方法:
- 推出了SPIRE,一个完全数字化的多水库小规模企业,在TSMC 28nm CMOS中适应在线学习.
- 实现了一个并行架构,最多有八个水库组合跨越四个核心.
- 利用飞行中的重量生成来减少内存占用量,并支持双重操作模式.
主要成果:
- 与之前的工作相比,达到了高达18.46×的突触密度改善.
- 证明了高的计算效率:3.56GSOP/mm2 (4.91 pJ/SOP) 在连续模式和76.05GSOP/mm2 (0.1 pJ/SOP) 在并行模式.
- 运行在55MHz和0.55V,展示功率和速度优势.
结论:
- SPIRE为神经形态计算任务 (如时间序列分类) 提供了一种内存高效和高性能解决方案.
- 设计的进步使复杂的LSM模型的实际边缘部署成为可能.
- 在克服脑启发计算的硬件局限性方面,SPIRE是迈出了重要的一步.
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