脑森林:神经形态的乘法器-少比特-序列重量-内存-优化1024-树脑状态分类处理器
IEEE transactions on biomedical circuits and systems
|October 16, 2024
概括
脑植入器处理器BrainForest实现了高能效的功能,用于检测病态大脑状态. 这项创新增强了个性化的治疗,通过启用机器学习来改善发作检测和治疗刺激.
科学领域:
- 神经技术和生物医学工程
- 神经形态计算是一种神经形态计算.
- 治疗的方法 治疗
背景情况:
- 个性化脑植入物为神经疾病治疗和认知增强提供了革命性的潜力.
- 目前的植入物需要低功耗的集成电路,这限制了先进的机器学习分类器的集成.
- 现有的设备需要节能解决方案,以持续,终身运行和改善治疗结果.
研究的目的:
- 介绍BrainForest,一个神经形态处理器,旨在在个性化植入物中节能地对大脑状态进行分类.
- 实现机器学习驱动的发作检测,以改善治疗中的治疗刺激.
- 为了克服阻碍可植入设备中先进计算能力的功率限制.
主要方法:
- 开发了一个无倍数,位序列,重量-内存优化的神经形态处理器架构.
- 使用了两层神经元模型:共振和发射用于EEG生物标志物提取,以及漏洞集成器用于多时间尺度分类.
- 实现了用于时钟门逻辑的稀疏神经发射活动,减少了93%的功耗.
- 整合了1024棵树增强的决策森林,用于病态大脑状态分类和刺激触发.
主要成果:
- 在65nm CMOS中实现了超低功耗 (最佳情况下9.6μW,典型的118μW) 的最先进的能效.
- 证明了高分类性能,97.5%的发作敏感度.
- 报告了每小时2.08的低错误检测率,对于可靠的治疗干预至关重要.
结论:
- 脑森林为芯片上大脑状态分类提供了高能效的解决方案,使低功耗植入物中的先进机器学习成为可能.
- 该架构通过稀疏的神经活动和优化设计显著降低了功耗.
- 这个处理器为更复杂和个性化的神经调节疗法铺平了道路,特别是在管理方面.
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