Collaborative Research: SHF: Small: Architecture Innovations for Enabling Simultaneous Translation at the Edge

合作研究：SHF：小型：支持边缘同步翻译的架构创新

• 批准号: 2223483
• 负责人: Lizhong Chen
• 金额: $ 45万
• 依托单位: Oregon State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-10-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2223483&HistoricalAwards=false
• 关键词: Collaborative; Research; SHF; Small; Architecture

Simultaneous translation, which begins translating after just a few words are spoken, has significant real-world value that may disrupt and benefit a wide range of domains, such as military personnel deployed in foreign countries, businesspeople participating in multilingual meetings, medical service providers, law enforcement, customer support services, diplomats and political representatives, and countless international tourists. Currently, accurate real-time simultaneous translation is only possible by expensive, specially trained human interpreters or by running machine learning-based algorithms on server-grade graphics processing units (GPUs); both options are impractical for extensive and ubiquitous deployment of simultaneous translation in edge devices. Innovative domain-specific architecture needs to be designed that can reduce computation requirements by orders of magnitude while maintaining translation accuracy, thus enabling simultaneous translation at the edge.This research investigates the challenges and opportunities in designing hardware accelerators for transformer-based simultaneous translation. The objective is to utilize the unique characteristics of transformer models and the distinctive behaviors of simultaneous translation to develop cross-cutting solutions that meet the accuracy, latency, power, and resource efficiency goals. Among some of the specific lines of research that are explored include input-centric dataflow and data reuse that take advantage of extensive input data sharing, compute-proportional architecture that aims to achieve linearly scalable attention calculation, utility-driven linear transformation architecture that efficiently reduce dimensionality based on their actual utility, and customized routerless on-chip interconnects that provide scalable, flexible and ultra-low cost interconnects for simultaneous translation accelerators. Beyond specific technical contributions that advance the fields of computer architecture and natural language processing, this project also impacts more broadly on research, education, and outreach. Findings from this research are incorporated into graduate curricula, courses, and undergraduate research experiences. Various outreach activities have been planned to broaden inclusion and participation of diverse populations in the educational and societal impacts of this project.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.

同声传译只需说几句话就能开始翻译，它具有重大的现实价值，可能会扰乱广泛的领域，并使其受益，例如部署在外国的军事人员、参加多语言会议的商人、医疗服务提供商、执法部门、客户支持服务、外交官和政治代表，以及无数的国际游客。目前，准确的实时同声翻译只能由昂贵的、经过专门培训的人工口译员或通过在服务器级图形处理单元(GPU)上运行基于机器学习的算法来实现；对于在边缘设备中广泛和普遍地部署同声翻译来说，这两种选择都是不切实际的。需要设计创新的特定领域的体系结构，在保持翻译精度的同时，将计算量减少几个数量级，从而在边缘实现同声翻译。其目的是利用变压器模型的独特特性和同声传译的独特行为来开发满足准确性、延迟、功耗和资源效率目标的横切解决方案。在所探索的一些特定研究领域中，包括利用广泛的输入数据共享的以输入为中心的数据流和数据重用、旨在实现线性可扩展注意力计算的计算比例体系结构、基于其实际效用有效降维的效用驱动的线性变换体系结构、以及为同声翻译加速器提供可扩展、灵活和超低成本的可扩展、灵活和超低成本的定制的无路由片上互连。除了推动计算机体系结构和自然语言处理领域的具体技术贡献外，该项目还对研究、教育和推广产生了更广泛的影响。这项研究的结果被纳入研究生课程、课程和本科生研究经验。已经计划了各种外展活动，以扩大不同人群对该项目的教育和社会影响的包容和参与。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Implicit Memory Transformer for Computationally Efficient Simultaneous Speech Translation

用于计算高效的同步语音翻译的隐式内存转换器

• DOI: 10.18653/v1/2023.findings-acl.816
• 发表时间: 2023
• 期刊: Findings of the Association for Computational Linguistics
• 作者: Raffel, Matthew;Chen, Lizhong
• 通讯作者: Chen, Lizhong

A Survey on Sparsity Exploration in Transformer-Based Accelerators

• DOI: 10.3390/electronics12102299
• 发表时间: 2023-05-19
• 期刊: ELECTRONICS
• 影响因子: 2.9
• 作者: Fuad, Kazi Ahmed Asif;Chen, Lizhong
• 通讯作者: Chen, Lizhong

Shiftable Context: Addressing Training-Inference Context Mismatch in Simultaneous Speech Translation

可移动上下文：解决同步语音翻译中的训练推理上下文不匹配问题

• 发表时间: 2023
• 期刊: International Conference on Machine Learning
• 作者: Raffel, Matthew;Penney, Drew;Chen, Lizhong
• 通讯作者: Chen, Lizhong