CSR: Small: Processing-in-Memory enabled Manycore Systems to Accelerate Graph Neural Network-based Data Analytics

Many science and engineering applications are enabled by processing large and heterogeneous graph structured data. For example, data from sensor feeds, information databases about events, supply chains, and web traffic are relational by nature and require graph-based representations. Graph Neural Networks (GNNs) will allow the analysis to uncover hidden patterns from the data and can enable these applications for making predictions and decision support.

Training machine learning (ML) models at the edge (training on-chip or on embedded systems) can address many pressing challenges, including data privacy/security, increase the accessibility of ML applications to different parts of the world by reducing the dependence on the communication fabric and the cloud infrastructure, and meet the real-time requirements of augmented/virtual reality (AR/VR) applications. Many applications including AR/VR require GNN training on embedded systems.

However, existing edge platforms do not have sufficient capabilities to support on-device training of GNNs. Moreover, it is estimated that training a single unpruned neural network on conventional compute platforms, such as GPUs, can cost over $10,000 and emit as much carbon as five cars over their lifetimes. Resistive random-access memory (ReRAM) based processing-in-memory (PIM) architectures are a promising solution to address this problem.

The crossbar structure of ReRAM-based architectures enables efficient Matrix-Vector Multiplication (MVM) operations, which are ubiquitous in modern ML tasks including GNN training/inference. We use ReRAM as an example, but the proposed computing framework will work equally well for any other crossbar-based PIM configuration. The educational contribution of this work lies in the establishment of an interdisciplinary research-based curriculum integrating PIM, machine learning, and data-driven design optimization.

The proposed research will enhance the education of students by enabling them to apply classroom knowledge to research problems that require hardware, software, and theoretical expertise. The PIs have many years of accumulated experience in involving underrepresented groups in research. This experience will be leveraged to motivate and engage students from underrepresented groups, including women, African Americans, and Hispanics.

In this project, we lay the foundations for a novel and reliable computing framework for GNN computation using PIM-based manycore systems. With the rising needs of GNN-based applications from the edge to the cloud, we need computing systems to meet the stringent size, weight, and power (SWaP) constraints. The key contribution of this research will be the conceptual development, optimization, and evaluation of high-performance, energy-efficient, and reliable PIM-based architectures for GNN computing.

Despite the exponential growth in interest and extensive research and application studies on GNN-based data analytics, the importance of hardware-assisted execution efficiency and hardware-aware algorithm efficiency has not received adequate attention. This research will reduce the dependency on data centers and high-performance computing (HPC) clusters for executing GNN-based applications.

There is a lack of holistic solutions that allow us to quickly design and optimize PIM-enabled computing platforms for GNNs. Hence, machine learning enabled hardware and software co-design optimization strategies proposed in this work will have profound impacts on computing platforms where GNNs are increasingly deployed. There is a growing set of edge ML applications that are enabled by deploying GNNs on mobile platforms or embedded systems.

Since mobile/embedded platforms are constrained by both compute and storage, there is a great need for PIM-based solutions to deploy GNNs for edge ML applications. More computational power at the edge will reduce both the internet traffic and power-hungry processing at data centers.

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.