BrainForest:神经形态乘法器--低比特序列权重--内存优化的 1024 树脑状态分类处理器

Gerard OLeary, Jamie Koerner, Mustafa Kanchwala, Jose Sales Filho, Jianxiong Xu, Taufik A Valiante, Roman Genov
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引用次数: 0

摘要

个性化脑部植入物有望彻底改变神经系统疾病的治疗方法,并增强认知能力。根据检测到的癫痫发作提供治疗性刺激的医疗植入体已被用于治疗癫痫。这些设备需要低功耗集成电路来实现终身运行。这种限制阻碍了机器学习驱动的分类器的集成,而机器学习驱动的分类器可以改善治疗效果。本文介绍的 BrainForest 是一种神经形态乘法器--无位串行权重内存优化脑状态分类处理器。该架构采用两层神经元模型来实现分类所需的频谱和时间函数,从而实现了最先进的能效:1)共振-发射神经元用于提取生理信号带能量脑电图生物标记;2)泄漏积分器神经元用于建立分类所需的多时间尺度表征。稀疏神经模型的发射活动被用于时钟门器件逻辑,从而将功耗降低了 93%。经过能量优化的 1024 树提升决策森林执行分类,用于根据检测到的大脑病理状态触发刺激。该集成电路采用 65nm CMOS 工艺实现,功耗达到最先进水平(最佳情况:9.6μW,典型情况:118μW),癫痫发作灵敏度达到 97.5%,错误检测率为每小时 2.08 次。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
BrainForest: Neuromorphic Multiplier-Less Bit-Serial Weight-Memory-Optimized 1024-Tree Brain-State Classification Processor.

Personalized brain implants have the potential to revolutionize the treatment of neurological disorders and augment cognition. Medical implants that deliver therapeutic stimulation in response to detected seizures have already been deployed for the treatment of epilepsy. These devices require low-power integrated circuits for life-long operation. This constraint impedes the integration of machine-learning driven classifiers that could improve treatment outcomes. This paper introduces BrainForest, a neuromorphic multiplier-less bit-serial weight-memory-optimized brain-state classification processor. The architecture achieves state-of-the-art energy efficiency using two layers of neuron models to implement the spectral and temporal functions needed for classification: 1) resonate-and-fire neurons are used to extract physiological signal band energy EEG biomarkers 2) leaky integrator neurons are used to build multi-timescale representations for classification. Sparse neural model firing activity is used to clock-gate device logic, thereby decreasing power consumption by 93%. An energy-optimized 1024-tree boosted decision forest performs the classification used to trigger stimulation in response to detected pathological brain states. The IC is implemented in 65nm CMOS with state-of-the-art power consumption (best case: 9.6μW, typical: 118μW), achieving a seizure sensitivity of 97.5% with a false detection rate of 2.08 per hour.

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