Open AccessDesign-Space Exploration of Application-specific Instruction-set Processor Design
Author(s) -
M. H. Sargolzaei
Publication year - 2021
Publication title -
computing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.184
H-Index - 11
eISSN - 2312-5381
pISSN - 1727-6209
DOI - 10.47839/ijc.20.4.2439
Subject(s) - computer science , design space exploration , efficient energy use , context (archaeology) , instruction set , set (abstract data type) , parallel computing , computer architecture , architecture , code (set theory) , key (lock) , energy (signal processing) , microarchitecture , space (punctuation) , embedded system , programming language , operating system , art , paleontology , statistics , mathematics , electrical engineering , visual arts , biology , engineering
Application-Specific Instruction-Set Processors (ASIPs) have established their processing power in the embedded systems. Since energy efficiency is one of the most important challenges in this area, coarse-grained reconfigurable arrays (CGRAs) have been used in many different domains. The exclusive program execution model of the CGRAs is the key to their energy efficiency but it has some major costs. The context-switching network (CSN) is responsible for handling this unique program execution model and is also one of the most energy-hungry parts of the CGRAs. In this paper, we have proposed a new method to predict important architectural parameters of the CSN of a CGRA, such as the size of the processing elements (PEs), the topology of the CSN, and the number of configuration registers in each PE. The proposed method is based on the high-level code of the input application, and it is used to prune the design space and increase the energy efficiency of the CGRA. Based on our results, not only the size of the design space of the CSN of the CGRA is reduced to 10%, but also its performance and energy efficiency are increased by about 13% and 73%, respectively. The predicted architecture by the proposed method is over 97% closer to the best architecture of the exhaustive searching for the design space.