Gorgin, S.; Karvandi, M.S.; Moghari, S.; Fallah, M.K.; Lee, J.-A. A Hardware Realization Framework for Fuzzy Inference System Optimization. Electronics2024, 13, 690.
Gorgin, S.; Karvandi, M.S.; Moghari, S.; Fallah, M.K.; Lee, J.-A. A Hardware Realization Framework for Fuzzy Inference System Optimization. Electronics 2024, 13, 690.
Gorgin, S.; Karvandi, M.S.; Moghari, S.; Fallah, M.K.; Lee, J.-A. A Hardware Realization Framework for Fuzzy Inference System Optimization. Electronics2024, 13, 690.
Gorgin, S.; Karvandi, M.S.; Moghari, S.; Fallah, M.K.; Lee, J.-A. A Hardware Realization Framework for Fuzzy Inference System Optimization. Electronics 2024, 13, 690.
Abstract
The effectiveness of Fuzzy Inference Systems (FISs) in manipulating uncertainty and nonlinearity makes them a subject of significant interest for decision-making in embedded systems. Accordingly, optimizing FIS hardware improves its performance, efficiency, and capabilities, leading to a better user experience, increased productivity, and cost savings. To be compatible with the limited power budget in most embedded systems, this paper presents a framework to realize ultra-low power FIS hardware. It supports optimizations for both conventional arithmetic as well as MSDF-computing to be highly consistent with MSDF-based sensors. In MSDF-computing FIS all the processes of fuzzification, inference, and defuzzification are done on serially coming data bits. To demonstrate the efficiency of the proposed framework, we utilized Matlab, Chisel3, and Vivado to implement it from high-level descriptions of FIS to hardware synthesis. We also developed a Scala library in Chisel3 to establish a connection between these tools, bridging the gap, and facilitating design space exploration at the arithmetic level. Furthermore, we realized an FIS for the navigation of autonomous mobile robots in unknown environments. Synthesis results show the superiority of the output of our suggested design framework in terms of resource usage as well as power and energy consumption compared to the Matlab HDL code generator output.
Keywords
Embedded System; Hardware Optimization; MSDF Computing; Fuzzy Inference System
Subject
Computer Science and Mathematics, Hardware and Architecture
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
