SHF: Small: Automated Algorithm/Hardware Co-Design for Accelerating Nanopore Base-calling

SHF：小型：加速纳米孔碱基识别的自动化算法/硬件协同设计

• 批准号: 1908992
• 负责人: Lei Jiang
• 金额: $ 49.89万
• 依托单位: Indiana University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-10-01 至 2023-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1908992&HistoricalAwards=false
• 关键词: SHF; Small; Automated; Algorithm; Hardware

Nanopore genome sequencing is becoming the cornerstone to enable personalized medicine, global food security and wildlife conservation. 'Base-calling' is the process of assigning bases (nucleobases) to signal peaks observed by the gene sequencer. It is the most time-consuming step during sequencing and uses deep neural networks to translate vast amounts of raw electrical signals produced by nanopore sequencers to digital DNA symbols. However, although current approaches reduce the computing overhead of a base-caller, they substantially increase uncorrectable systematic errors resulting in low accuracy for base-calling. Moreover, the low power efficiency of prior base-calling accelerators severely restrict the use of this sequencing technology in numerous real-time biomedical applications. Currently, there is no methodology to automatically explore the huge design space of a base-caller in conjunction with its hardware accelerators. To remedy these issues, this project will develop a novel algorithm & hardware co-design methodology to make nanopore base-calling more power efficient, scalable and accessible, making it possible to realize its value in socially relevant applications that demand fast genome sequencing solutions. It will also lower the barrier to nanopore sequencing development, bringing the benefits of sequencing to users who are not machine learning and hardware design experts. This project will engage undergraduate and graduate students in cutting edge interdisciplinary fields ranging from genomics to computer engineering. All artifacts and teaching materials will be broadly disseminated via open source and creative commons.This project aims to develop and rigorously validate (1) a systematic-error-aware binarized base-caller, (2) a power-efficient spintronics accelerator for binarized base-calling, and (3) deep-reinforcement-learning-based approach to automatically explore the design space of the base-caller and its accelerator, to generate optimal frameworks for developing the next generation of base-callers. These three aims will enable even non-experts to take advantage of nanopore base-calling in numerous life science fields.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.

纳米孔基因组测序正在成为实现个性化医疗、全球粮食安全和野生动物保护的基石。“碱基识别”是将碱基（核碱基）分配给基因测序仪观察到的信号峰的过程。它是测序过程中最耗时的步骤，使用深度神经网络将纳米孔测序仪产生的大量原始电信号转换为数字DNA符号。然而，尽管当前的方法减少了碱基识别的计算开销，但是它们实质上增加了不可校正的系统误差，导致碱基识别的低准确性。此外，现有碱基识别加速器的低功率效率严重限制了这种测序技术在许多实时生物医学应用中的使用。目前，还没有一种方法可以自动探索与其硬件加速器相结合的基础调用程序的巨大设计空间。为了解决这些问题，该项目将开发一种新的算法硬件协同设计方法，使纳米孔碱基调用更具功率效率，可扩展性和可访问性，使其在需要快速基因组测序解决方案的社会相关应用中实现其价值成为可能。它还将降低纳米孔测序开发的障碍，为非机器学习和硬件设计专家的用户带来测序的好处。该项目将吸引本科生和研究生参与从基因组学到计算机工程的尖端跨学科领域。本项目旨在开发并严格验证（1）系统错误感知的二进制碱基调用器，（2）用于二进制碱基调用的节能自旋电子加速器，以及（3）基于深度强化学习的方法，以自动探索碱基调用器及其加速器的设计空间，为开发下一代碱基识别者提供最佳框架。这三个目标将使即使非专家利用纳米孔基地呼吁在许多生命科学领域。这个奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

项目成果

Mitigating Voltage Drop in Resistive Memories by Dynamic RESET Voltage Regulation and Partition RESET

• DOI: 10.1109/hpca47549.2020.00031
• 发表时间: 2020-02
• 期刊: 2020 IEEE International Symposium on High Performance Computer Architecture (HPCA)
• 作者: Farzaneh Zokaee;Lei Jiang

SHE: A Fast and Accurate Deep Neural Network for Encrypted Data

SHE：一种快速准确的加密数据深度神经网络

• 发表时间: 2019
• 期刊: Advances in neural information processing systems
• 作者: Lou, Qian;Jiang, Lei
• 通讯作者: Jiang, Lei

AutoQ: Automated Kernel-Wise Neural Network Quantization

• 发表时间: 2019-02
• 期刊: arXiv: Learning
• 作者: Qian Lou;Feng Guo;Lantao Liu;Minje Kim;Lei Jiang

PriML: An Electro-Optical Accelerator for Private Machine Learning on Encrypted Data

PriML：用于加密数据私人机器学习的光电加速器

• DOI: 10.1109/isqed57927.2023.10129302
• 发表时间: 2023
• 期刊: IEEE International Symposium on Quality Electronic Design
• 作者: Zheng, Mengxin;Chen, Fan;Jiang, Lei;Lou, Qian
• 通讯作者: Lou, Qian

Glyph: Fast and Accurately Training Deep Neural Networks on Encrypted Data

Glyph：在加密数据上快速准确地训练深度神经网络

• 发表时间: 2020
• 期刊: Advances in neural information processing systems
• 作者: Qian, Lou;Bo, Feng;Geoffrey, Fox;Lei, Jiang
• 通讯作者: Lei, Jiang