ASCENT: Collaborative Research: Scaling Distributed AI Systems based on Universal Optical I/O

我们的社会正迅速依赖于基于神经网络的人工智能计算。每天都有新的算法被发明出来，这增加了推理和训练的内存和计算需求。这种爆炸性的增长创造了对分布式机器学习（ML）训练和推理的巨大需求。OpenAI的估计表明，自2012年以来，计算需求每两年稳定增长100倍，这比我们在过去半个世纪中通过半导体行业的摩尔定律实现的计算改进速度快50倍。硬件加速器和软件栈的快速发展部分地满足了这种新的计算需求，以支持这些专门的计算。硬件加速器提供了显著的加速，但今天的训练任务仍然需要几天甚至几周的时间。其原因是：随着工作人员（例如计算节点）数量的增加，每个工作人员的计算时间减少，但节点之间的通信需求增加，从而在计算节点之间的互连中产生瓶颈。未来的分布式机器学习系统将需要每个节点高1-2个数量级的互连带宽，这就迫切需要为分布式机器学习系统构建全新的互连方式。该提案旨在通过开发基于推进集成电子和光子学技术的可扩展互连解决方案，实现直接节点到节点光纤连接，为扩展分布式机器学习计算创建一个新的范例。拟议的跨堆栈协作多学科工作将使教育和培训一批独特的工程师和科学家，这些工程师和科学家跨越机器学习，网络和电子-光子系统和设备的边界，这些都是迫切需要的。主要研究人员在伯克利无线研究中心和麻省理工学院的女性技术项目中与高中生直接接触，提供暑期实习机会，并在波士顿大学开展示范性的本科生研究活动。pi开展的教育和推广活动将确保该领域新一代领导者的早期接触和持续培训，从K-12到本科和研究生学习，以及继续劳动力教育，特别关注代表性不足的学生。互连已成为实现分布式机器学习全部潜力的关键瓶颈。未来的机器学习工作负载可能需要每个设备数十Tbps的带宽。跨货架、机架和行规模的逻辑连接、物理分布式计算的无处不在的部署，只能通过一种新的通用I/O来实现，这种通用I/O可以在封装互连的能量、延迟和带宽密度上实现套接字到套接字的通信。目前还不存在这样的技术。基于硅光子学的光学I/O有潜力解决这一关键挑战，但仍然需要从芯片制造到路由算法的基本进步来确保这些互连系统的可扩展性。为了实现高带宽密度和高能效，必须使用密集波分复用。高效率的环形谐振器调制器和梳状激光源需要在每个光纤连接上实现Tbps速率，插座带宽从10s扩展到1000s Tbps。需要新的链路架构，如所提出的激光转发相干链路，以实现每个光纤端口每个波长的适度（亚毫瓦）功率的高效外部集中式梳状激光源。拟议的工作还将开发新的调度算法、网络架构和工作负载并行策略，以利用通用光I/O的带宽密度和低延迟，将具有大量数据集的大型人工智能工作负载映射到可扩展的分布式计算系统。该奖项反映了美国国家科学基金会的法定使命，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。

Our society is rapidly becoming reliant on neural networks based artificial intelligence computation. New algorithms are invented daily, increasing the memory and computational requirements for both inference and training. This explosive growth has created an enormous demand for distributed machine learning (ML) training and inference. Estimates by OpenAI illustrate the steady growth of computational requirements of 100x every two years since 2012, which is a 50x faster than the rate of computation improvements enabled previously through Moore’s Law of semiconductor industry that we have enjoyed in the last half-century. This new computation demand has been partly met by rapid development of hardware accelerators and software stacks to support these specialized computations. Hardware accelerators have provided a significant amount of speed-up but today’s training tasks can still take days and even weeks. The reason for this: as the number of workers (e.g. compute nodes) increases, the computation time per worker decreases, but the communication requirements between the nodes increase, creating a bottleneck in the interconnect between the compute nodes. Future distributed ML systems will require 1-2 orders of magnitude higher interconnect bandwidth per node, creating a pressing need for entirely new ways to build interconnects for distributed ML systems. This proposal aims to create a new paradigm for scaling distributed ML computation, by developing a scalable interconnect solution based on advancing the integrated electronics and photonics technology that enables direct node-to-node optical fiber connectivity. The proposed cross-stack collaborative multi-disciplinary work will enable the education and training of a unique crop of engineers and scientists that cross the boundaries of machine learning, networking, and electronic-photonic systems and devices, which are in severe demand. The principal investigators have an established track record of direct engagement with high-school students providing summer internships at Berkeley Wireless Research Center and MIT’s Women’s Technology Program, as well as exemplary undergraduate research activities at Boston University. The educational and outreach activities the PIs have put in place will ensure early exposure and continued training of new generation of leaders in this field, from K-12, through undergraduate and graduate studies, and continuing workforce education, with special focus on underrepresented students.The interconnect has emerged as the key bottleneck in enabling the full potential of distributed ML. Future ML workloads are likely to require tens of Tbps of bandwidth per device. Ubiquitous deployment of logically-connected, physically distributed computation across shelf, rack and row scale can only be enabled by a new universal I/O that enables socket to socket communication at the energy, latency and bandwidth density of in-package interconnects. No such technology currently exists. Silicon-photonics based optical I/O has the potential to address this critical challenge, but fundamental advances–from chip manufacturing to routing algorithms–are still needed to ensure the scalability of these interconnect systems. To enable high-bandwidth density and energy-efficiency, dense wavelength division multiplexing must be used. High-efficiency ring resonator-based modulators and comb laser sources are needed to enable Tbps rates over each fiber connection and socket bandwidth scaling from 10s to 100s of Tbps. New link architectures like the proposed laser-forwarded coherent link are needed to enable high-efficiency external centralized comb laser sources with modest (sub-mW) power per wavelength per fiber port. The proposed work will also develop new scheduling algorithms, network architectures, and workload parallelism strategy to leverage the bandwidth density and low-latency of the universal optical I/O, to map large AI workloads with massive datasets to a scalable distributed compute system.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.