Collaborative Research: CNS Core: Small: NV-RGRA: Non-Volatile Nano-Second Right-Grained Reconfigurable Architecture for Data-Intensive Machine Learning and Graph Computing

George Mason University, Fairfax VA

Investigators

Abstract

In the era of digital data, computing devices amass vast amounts of data continuously over time. This leads to a paradigm shift in the computing by adopting machine learning (ML) and graph analytic processing to analyze such massive amounts of data. Traditional computing paradigms are inefficient in terms of energy consumption, latency, and computational efficiency. Existing in-memory computing paradigms address this challenge to a certain extent, but are not adaptable for heterogeneous applications. The proposed project introduces a novel computing architecture, right-grained reconfigurable architecture (RGRA) that combines the flexibility of coarse-grained reconfigurable array (CGRA) and programmability of FPGAs, deploying a circuit-switched interconnects and router network with torus topology to address this challenge. At the top-level, RGRA is a many-core architecture with each core configurable at finer granularity. The proposed research is also expected to lead to the development of new branch of reconfigurable architectures that support efficient execution of data-intensive applications such as graph analytics and benefit from architectural aspects such as reconfigurability and heterogeneity. In terms of broader impact, design of hardware accelerators is one of the driving directions in the field of computer architecture. As such, the successful design of RGRA that is performance efficient irrespective of the application memory-traits can have a significant societal and economic impact. For instance, it can augment CPUs, FPGAs, and GPUs in the existing and emerging systems. The results of the project will include design of high-speed reconfigurable NVMs and interconnects, which can also be adopted in many-core systems towards developing high-throughput processors. With ML being taught in the higher-secondary schools, the project has a good scope for outreach to the community and attract students, especially in terms of (i) recruitment of underrepresented classes including minorities and women; (ii) outreach in the form of K-12 and undergraduate student involvement in research via summer internships and senior-design projects; and (iii) offering a graduate course on ML accelerator design. 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.