MITRACA: A Next-Gen Heterogeneous Architecture

Abstract

GPU (Graphics Processing Unit) and CPU (Central Processing Unit) possess a sufficient and appropriate performance to compute massively parallel applications like AI, Big data, and material sciences. However, their real performance is far lower than those theoretical ones. The primary reason for the performance degradation is that they suffer from limited memory bandwidth and inefficient interconnection topology not optimized for these types of applications. Thus, from the viewpoint of real computational performance called computational efficiency, FPGA (Field Programmable Gate Array) is now becoming an attractive chip for these types of applications with massively parallel computation. FPGA can efficiently propose optimized communication and bridge different computing accelerators as customized hardware. In other words, FPGA-based hardware accelerators offer a convenient solution for both high performance and high memory bandwidth. However, one serious concern is usability. For example, the FPGA design using hardware description language is a meticulous task and requires specialized skill sets as well as a long time to market. An overlay architecture will become an appropriate candidate that can resolve this issue because it offers a software layer that simplifies FPGA programmability by abstracting the fabric resources. Thus, this article proposes an overlay architecture based on a tightly-connected many-core-based CGRA (Coarse-Grained Reconfigurable Architecture). It will help software engineers on seamlessly implementing their applications. Our final goal is not on the current fine-grained FPGAs but new middle-to-course-grained programmable chips. If an ASIC (Application-Specific Integrated Circuit) implementation was adopted, the performance would achieve at least ten times higher compared with the current FPGA implementation because of the working frequency. In this article, the proposed overlay system provides a programmable interface that virtualizes FPGA resources and let prospected users focus on high-level software programming.