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: 2223484
• 负责人: Timothy Pinkston
• 金额: $ 15万
• 依托单位: University of Southern California
• 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=2223484&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.

同时翻译仅在说几句话后开始翻译，具有显着的现实价值，可能会破坏和受益于广泛的领域，例如部署在国外的军事人员，商业人士，参加多语言会议，医疗服务提供者，医疗服务提供者，执法服务，客户支持服务，外交官和政治代表以及无数的国际旅游者以及无数的国际旅游。当前，只有昂贵的，经过专门训练的人类口译员或在服务器级图形处理单元（GPU）上运行基于机器学习的算法才能进行准确的实时同时翻译；这两种选项对于在边缘设备中同时翻译的广泛和无处不在的部署都是不切实际的。需要设计创新的域特异性体系结构，以通过数量级来减少计算要求，同时保持翻译精度，从而在边缘同时进行翻译。这项研究研究了针对基于变压器的同时翻译设计硬件加速器的挑战和机会。目的是利用变压器模型的独特特征以及同时翻译的独特行为来开发跨切换解决方案，以满足准确性，潜伏期，功率和资源效率目标。在探索的一些特定研究线中，包括以输入为中心的数据流量和数据重用，利用广泛的输入数据共享，计算构造，旨在实现可线性可扩展的注意力计算，实用性驱动的线性变换体系结构，这些线性转换体系结构有效地基于其实际的实用性，可固定的稳定性且可固定的稳定性，从而有效地降低尺寸用于同时翻译加速器。除了推进计算机架构和自然语言处理领域的特定技术贡献之外，该项目还对研究，教育和外展更广泛地影响。这项研究的结果纳入了研究生课程，课程和本科研究经验中。已经计划开展各种宣传活动，以扩大和参与该项目的教育和社会影响。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和更广泛影响的评估评估的评估值得支持的。