Architectural frameworks to leverage new hardware technologies for emerging data-intensive applications

利用新硬件技术实现新兴数据密集型应用程序的架构框架

• 批准号: RGPIN-2021-03542
• 负责人: Vijaykumar, Nandita
• 金额: $ 2.11万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=741843
• 关键词: Architectural; frameworks; leverage; new; hardware

Important emerging data-intensive applications in machine learning and robotics utilize computing systems at different levels: datacenters, edge devices, and on resource-constrained autonomous vehicles (e.g., drones). These applications require processing significant amounts of data under various system constraints and their scale and complexity are today limited by constraints in memory, compute, and network capabilities. Advances in hardware technologies such as disaggregated memory, near-data processing, and application-specific acceleration offer promising opportunities to address these bottlenecks. Leveraging these technologies effectively requires co-designing algorithms, systems, and architectures. This research program aims to develop new architectural frameworks and cross-layer solutions to leverage new hardware technologies in three contexts: data centers, edge devices, and low-power autonomous vehicles. The proposed research aims to tackle the following major thrusts. First, we aim to investigate the system challenges to efficiently incorporate memory disaggregation in datacenters. Disaggregation rethinks the traditional notion of datacenters comprising monolithic servers with memory attached over the memory bus. Instead processors are connected to network-attached pools of memory that are independently operated. Disaggregation offers an opportunity to meet the significant memory needs of emerging data-intensive applications such training of large-scale machine learning models at low cost. Second, we aim to investigate the implications of the push towards edge-based computing in large-scale data-intensive applications. Important applications for machine learning in health care, mobile applications, financial institutions require privacy of user data, necessitating partial training of data on edge devices/servers. These applications typically use federated and incremental training to train machine learning models while preserving the privacy of user data. We aim to tackle the architectural and programmability challenges associated with efficient deployment of federated learning in edge devices. Third, we aim to investigate the architectural challenges of supporting robotics tasks on resource-constrained autonomous vehicles such as Unmanned Aerial Vehicles (UAVs) and vehicles for micromobility. These systems are projected to have tremendous growth in demand with use cases in healthcare, rescue, delivery, and mobility. These vehicles are required to support data-intensive tasks such as processing sensor data from LIDAR, cameras, etc., complex localization and mapping, and DNN inference, while still being heavily constrained in power and compute capability. This thrust will involve 1) investigating key compute bottlenecks for each task; 2) developing infrastructure to enable cross-layer research for each application domain; 3) developing hardware-software co-designs to enable efficient processing of these tasks.

机器学习和机器人技术中的重要新兴数据密集型应用程序使用不同级别的计算系统：数据中心，边缘设备以及资源约束的自动驾驶汽车（例如无人机）。这些应用程序需要在各种系统限制下处理大量数据，并且它们的规模和复杂性今天受到内存，计算和网络功能的限制的限制。硬件技术的进步，例如分解记忆，近数据处理和特定于应用程序的加速度，为解决这些瓶颈提供了有希望的机会。有效利用这些技术需要共同设计算法，系统和体系结构。该研究计划旨在开发新的建筑框架和跨层解决方案，以在三种情况下利用新的硬件技术：数据中心，边缘设备和低功率自动驾驶汽车。拟议的研究旨在应对以下主要推力。首先，我们旨在调查系统的挑战，以有效地将内存分解在数据中心中。分解重新考虑了传统的数据中心概念，其中包括整体服务器，并在内存总线上附上内存。相反，处理器连接到独立操作的网络连接池。分解提供了一个机会，可以满足新兴数据密集型应用程序的重大记忆需求，例如以低成本对大型机器学习模型进行培训。 其次，我们旨在研究大规模数据密集型应用程序中推动基于边缘计算的启示。医疗保健，移动应用程序，金融机构中机器学习的重要应用需要用户数据的隐私，因此需要对Edge设备/服务器上的数据进行部分培训。这些应用程序通常使用联合和增量培训来培训机器学习模型，同时保留用户数据的隐私。我们旨在应对与边缘设备中联合学习有效部署相关的建筑和可编程性挑战。 第三，我们旨在调查支持机器人技术在资源约束的自动驾驶汽车（例如无人驾驶汽车（UAV）（UAV）和车辆以进行微型行为方面的建筑挑战。这些系统预计，随着医疗保健，救援，交付和流动性的用例，需求的需求量巨大。这些车辆需要支持数据密集型任务，例如来自LiDAR，相机等的处理传感器数据，复杂的本地化和映射以及DNN推断，同时仍受到严重限制的功率和计算能力。该推力将涉及1）调查每个任务的关键计算瓶颈； 2）开发基础架构以实现每个应用领域的跨层研究； 3）开发硬件软件共同设计，以实现这些任务的有效处理。