SHF: Small: Architectural Techniques for Energy-Efficient Brain-Machine Implants

SHF：小型：节能脑机植入物的架构技术

• 批准号: 1815718
• 负责人: Abhishek Bhattacharjee
• 金额: $ 46.6万
• 依托单位: Rutgers University New Brunswick
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-10-01 至 2020-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1815718&HistoricalAwards=false
• 关键词: SHF; Small; Architectural; Techniques; Energy

This project focuses on the development of neural prostheses or brain implants to advance the scientific community's understanding of how the brain works, and to take a step towards devising treatment for neurological disorders. Brain implants are devices that are surgically embedded under the skull (of animals or humans in the context of scientific experiments and treatment of neurological disorders respectively) and placed on brain tissue, where they stimulate and record from hundreds of neurons. These devices are being used today to record neuronal electro-physiological data to unlock mysteries of the brain; to treat symptoms of Parkinson's disease, Tourette's syndrome, and epilepsy, with techniques like deep brain stimulation; and to offer treatment to those afflicted by paralysis or spinal cord damage via motor cortex implants. A key design issue with brain implants is that they are highly energy constrained, because they are embedded under the skull, and techniques like wireless power can heat up the brain tissue surrounding the implant. This project offers architectural techniques to lower the power consumption and energy usage of processing elements integrated on brain implants, whether they are general-purpose processors, customized integrated circuits, or programmable hardware. In tandem with its scientific studies, this project integrates an educational component to train high-school students, undergraduates, and PhD students on neuro-engineering techniques crucial to the society's continued efforts to shed light on how the brain works. In terms of technical details, this project performs the first study on architectural techniques to improve the energy efficiency of embedded processors on implants by leveraging their existing low-power modes. Low-power modes can be used in the absence of interesting neuronal activity, which corresponds to periods of time when the implant is not performing useful work and the processor can be slowed down. A critical theme of this project is to show that hardware traditionally used to predict program behavior (e.g., branches or cache reuse) can also be co-opted to also predict brain activity, and hence anticipate interesting/non-interesting neuronal spiking. Such predictors can consequently be used to drive the implant processor in and out of low power mode. This project studies how to design hardware brain activity predictors that predict neuronal activity accurately, scalably, and efficiently, and how to integrate such predictors with low power modes on commodity embedded processors. The techniques are drawn from hardware machine-learning approaches for program prediction and consider neuronal spiking data extracted from brain sites on mice, sheep, and monkeys. Successful deployment of these approaches is expected to save as much as 85% of processor energy, effectively quadrupling battery lifetimes on implants being designed for mice.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该项目着重于神经假体或大脑植入物的发展，以促进科学界对大脑运作方式的理解，并迈出一步，朝着设计神经系统疾病的治疗方案。脑植入物是在科学实验和神经系统疾病的治疗中分别将手术嵌入在颅骨下（动物或人类）并置于脑组织中的装置，它们会刺激数百个神经元并记录下来。如今，这些设备被用来记录神经元电体生理数据，以解锁大脑的奥秘。治疗帕金森氏病，图雷特综合症和癫痫的症状，并采用深脑刺激等技术；并为通过瘫痪或通过运动皮质植入物造成脊髓损伤的人提供治疗。大脑植入物的一个关键设计问题是它们受到高能量的约束，因为它们嵌入了头骨下，而无线功率等技术可以加热植入物周围的脑组织。该项目提供了建筑技术，以降低集成在大脑植入物上的处理元件的功耗和能源使用，无论它们是通用处理器，定制的集成电路还是可编程硬件。与科学研究同时，该项目将教育组成部分整合到培训高中生，本科生和博士学位学生的神经工程技术方面，这对于社会继续努力阐明大脑的运作方式至关重要。就技术细节而言，该项目通过利用现有的低功率模式来提高植入物上嵌入式处理器的能源效率的首次研究，以提高植入物上嵌入式处理器的能效。低功率模式可以在没有有趣的神经元活性的情况下使用，这对应于植入物没有执行有用工作并且处理器可以放慢速度的时间。该项目的一个关键主题是表明传统上用于预测程序行为的硬件（例如，分支或缓存重用）也可以选择以预测大脑活动，因此可以预测有趣的/非房屋的神经元峰值。因此，可以使用此类预测因素将植入物处理器驱动到低功率模式。该项目研究了如何设计硬件大脑活动预测因子，这些预测能够准确，可扩展，有效地预测神经元活动，以及如何将此类预测因子与商品嵌入式处理器上的低功率模式整合在一起。这些技术来自用于程序预测的硬件机器学习方法，并考虑从小鼠，绵羊和猴子上脑站点提取的神经元尖峰数据。预计这些方法的成功部署将节省多达85％的处理器能源，从而有效地将电池寿命四倍地寿命为用于小鼠设计的植入物。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的审查标准通过评估来获得支持的。

项目成果

HALO: A Hardware–Software Co-Designed Processor for Brain–Computer Interfaces

HALO：针对大脑与计算机接口的硬件与软件联合设计的处理器

• DOI: 10.1109/mm.2023.3258907
• 发表时间: 2023
• 期刊: IEEE Micro
• 影响因子: 3.6
• 作者: Sriram, Karthik;Karageorgos, Ioannis;Wen, Xiayuan;Veselý, Ján;Lindsay, Nick;Wu, Michael;Khazan, Lenny;Pothukuchi, Raghavendra Pradyumna;Manohar, Rajit;Bhattacharjee, Abhishek
• 通讯作者: Bhattacharjee, Abhishek