SHF: Small: Energy-Efficient and Reliable Communication with Silicon Photonics for Terascale Datacenters-on-Chip

SHF：小型：采用硅光子技术实现兆兆级片上数据中心的节能且可靠的通信

• 批准号: 1813370
• 负责人: Sudeep Pasricha
• 金额: $ 45万
• 依托单位: Colorado State University
• 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 2022-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1813370&HistoricalAwards=false
• 关键词: SHF; Small; Energy; Efficient; Reliable

Electronic processing chips are at the heart of the digital intelligence that has been the driving force for groundbreaking technological advances across the medical, consumer, industrial, networking, aerospace, automotive, and defense application domains. In recent years, there has been a growing trend in these application domains of massive data generation and consumption, which puts immense pressure on the networks at the chip-scale that must now transfer very high volumes of data in much shorter durations of time than ever before. Traditional electrical networks at the chip-scale are breaking down under this pressure, which is catastrophic as it prevents the development of the next generation of high-performance digital intelligence that can transform society and improve lives. Fortunately, silicon photonics has emerged as an exciting technological panacea that can replace slow electrical links with much faster light-speed transfers. While communication over long optical fibers (e.g., several miles) is quite common today, the nano-integration of silicon photonics technology with electronic chips is a new paradigm and presents enormous challenges that have yet to be addressed. This project will involve transformative research to overcome these fundamental challenges, and pave the way for realizing future photonics-based electronic chips that are miniature in size, but with the same computing power as a large datacenter computing facility today. Close collaborations with industrial partners at HP Enterprise and Lumerical will aid in the rapid adoption of the outcomes. Moreover, by exposing K-12, undergraduate, and graduate students to the diverse aspects of emerging technologies, devices, circuits, architectures, and algorithms, the project will contribute to an agile high-tech workforce that will maintain continued US leadership in technological innovation.The principal contribution of this project will be a new framework that will push the boundaries of achieving ultra-low energy and high reliability data transfers with silicon photonics at the chip-scale. This framework consists of three major thrusts that are closely related and will be addressed in a highly integrated manner: (1) Characterize behavior of silicon photonics devices and explore new device configurations based on device fabrication at Applied NanoTools Inc., to enable the selection of energy-efficient and low-cost devices; (2) Design new circuits with silicon photonics devices to overcome noise, increase bandwidth, and reduce power dissipation during communication; and (3) Create new silicon photonics-based network architectures and tools for their optimization, to realize ultra-low energy and fault-resilience solutions for transferring data between processing cores at the chip-scale. Beyond these three thrusts, the framework will exploit cross-layer insights across the device, circuit, and architecture layers, and devise optimizations that span across two or more of these layers. The innovations at the individual layers together with optimizations across layers will achieve more aggressive energy savings and higher reliability chip-scale communication than what is possible today. This outcome will usher in a new era of ultra-high performance computing where light speed data transfers within electronic chips can work together with optics-based data transfers external to the chip, to overcome communication energy and performance bottlenecks at all levels. Such a development will enable lower-cost supercomputing and cloud datacenters, making computing more affordable for scientists and more ubiquitous in everyday life, to transform our lives in innumerable ways.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.

电子处理芯片是数字智能的核心，是医疗、消费、工业、网络、航空航天、汽车和国防应用领域突破性技术进步的驱动力。近年来，在这些应用领域中，大规模数据生成和消费的趋势越来越明显，这给芯片级网络带来了巨大的压力，现在必须在比以往更短的时间内传输大量数据。传统的芯片级电力网络在这种压力下正在崩溃，这是灾难性的，因为它阻止了下一代高性能数字智能的发展，这些智能可以改变社会和改善生活。幸运的是，硅光子学已经成为一种令人兴奋的技术灵丹妙药，可以用更快的光速传输取代缓慢的电子连接。而在长光纤上的通信（例如，虽然纳米集成技术（例如，几英里）在今天非常普遍，但硅光子技术与电子芯片的纳米集成是一种新的范例，并提出了尚未解决的巨大挑战。该项目将涉及变革性的研究，以克服这些根本性的挑战，并为实现未来基于光子学的电子芯片铺平道路，这些电子芯片尺寸微小，但具有与当今大型数据中心计算设施相同的计算能力。与HP Enterprise和Lumerical的工业合作伙伴密切合作将有助于快速采用这些成果。此外，通过将K-12，本科生和研究生暴露于新兴技术，设备，电路，架构和算法的各个方面，该项目将有助于建立一支灵活的高科技劳动力队伍，以保持美国在技术创新方面的持续领先地位。该项目的主要贡献将是一个新的框架，该框架将推动实现超低能量和高可靠性的数据传输与硅光子在芯片级。该框架包括三个密切相关的主要目标，并将以高度集成的方式加以解决：（1）表征硅光子器件的行为，并基于Applied NanoTools Inc.的器件制造探索新的器件配置，这些技术包括：（1）设计新的硅光子器件电路，以克服噪声，增加带宽，降低通信过程中的功耗;（2）创建新的基于硅光子的网络架构和优化工具，以实现超低能耗和故障恢复解决方案，用于在芯片级处理核心之间传输数据。除了这三个方面，该框架还将利用跨器件、电路和架构层的跨层洞察力，并设计跨越两个或更多层的优化。各个层的创新以及跨层的优化将实现比今天更积极的节能和更高的可靠性芯片级通信。这一成果将开创一个超高性能计算的新时代，在这个时代，电子芯片内的光速数据传输可以与芯片外部的基于光学的数据传输协同工作，以克服各个层面的通信能量和性能瓶颈。这样的发展将使更低成本的超级计算和云计算中心成为可能，使计算对科学家来说更实惠，在日常生活中更无处不在，以无数方式改变我们的生活。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值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

Securing Photonic NoC Architectures from Hardware Trojans

保护光子 NoC 架构免受硬件木马的侵害

• 发表时间: 2018
• 期刊: International Symposium on Networks-on-Chip
• 作者: Pasricha, S.;Chittamuru, S. V.;Thakkar, I.;Bhat, V.
• 通讯作者: Bhat, V.

LORAX: Loss-Aware Approximations for Energy-Efficient Silicon Photonic Networks-on-Chip

• DOI: 10.1145/3386263.3406919
• 发表时间: 2020-02
• 期刊: Proceedings of the 2020 on Great Lakes Symposium on VLSI
• 作者: Febin P. Sunny;Asif Mirza;Ishan G. Thakkar;S. Pasricha;M. Nikdast

Interconnects for DNA, Quantum, In-Memory, and Optical Computing: Insights From a Panel Discussion

• DOI: 10.1109/mm.2022.3150684
• 发表时间: 2022-05-01
• 期刊: IEEE MICRO
• 影响因子: 3.6
• 作者: Ganguly, Amlan;Abadal, Sergi;Taskin, Baris
• 通讯作者: Taskin, Baris

A Survey of Resource Management for Processing-in-Memory and Near-Memory Processing Architectures

• DOI: 10.3390/jlpea10040030
• 发表时间: 2020-09
• 期刊: ArXiv
• 作者: Kamil Khan;S. Pasricha;R. Kim

A Survey on Optical Phase-Change Memory: The Promise and Challenges

• DOI: 10.1109/access.2023.3241146
• 发表时间: 2023
• 期刊: IEEE Access
• 影响因子: 3.9
• 作者: Amin Shafiee;S. Pasricha;M. Nikdast