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

机器学习和机器人技术中重要的新兴数据密集型应用在不同层面上利用计算系统：数据中心、边缘设备和资源受限的自动驾驶车辆（例如，无人机）。这些应用程序需要在各种系统约束下处理大量数据，其规模和复杂性目前受到内存、计算和网络能力的限制。硬件技术的进步，如分散式内存、近数据处理和特定于应用程序的加速，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、系统和架构。该研究计划旨在开发新的架构框架和跨层解决方案，以在三个环境中利用新的硬件技术：数据中心，边缘设备和低功耗自动驾驶汽车。拟议的研究旨在解决以下主要问题。首先，我们的目标是调查系统的挑战，以有效地将内存分解在数据中心。分解重新思考了数据中心的传统概念，数据中心由通过内存总线连接内存的单片服务器组成。相反，处理器连接到独立操作的网络附加内存池。分解提供了一个机会，以满足新兴的数据密集型应用程序的显着内存需求，如以低成本训练大规模机器学习模型。 其次，我们的目标是研究在大规模数据密集型应用中推动基于边缘的计算的影响。医疗保健、移动的应用、金融机构中的机器学习的重要应用需要用户数据的隐私，这需要在边缘设备/服务器上对数据进行部分训练。这些应用程序通常使用联合和增量训练来训练机器学习模型，同时保护用户数据的隐私。我们的目标是解决与在边缘设备中有效部署联邦学习相关的架构和可编程性挑战。 第三，我们的目标是研究在资源受限的自动驾驶车辆上支持机器人任务的架构挑战，例如无人机（UAV）和微移动车辆。这些系统预计将在医疗保健，救援，交付和移动性等用例中有巨大的需求增长。这些车辆需要支持数据密集型任务，例如处理来自激光雷达、相机等的传感器数据，复杂的定位和映射以及DNN推理，同时仍然在功率和计算能力方面受到严重限制。这一目标将涉及：1）调查每个任务的关键计算瓶颈; 2）开发基础设施，以便为每个应用领域进行跨层研究; 3）开发硬件-软件协同设计，以便有效处理这些任务。

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.