A fast MILP solver for high-level synthesis based on heuristic model reduction and enhanced branch and bound algorithm

Mirhosseini, Mina; Fazlali, Mahmood; Fallah, Mohammad K; Lee, Jeong-A

doi:10.1007/s11227-023-05109-2

Cited by 3 publications

(1 citation statement)

References 62 publications

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“…To optimize hardware through its high-level description, a range of optimization techniques can be employed across different levels of granularity, from coarse-grained levels such as task graphs to fine-grained levels such as data flow graphs [19,20]. Computer arithmetic provides a suite of efficient methods and tools for minimizing the processing requirements of specific tasks, particularly at the fine-grained level [21][22][23].…”

Section: Introductionmentioning

confidence: 99%

A Hardware Realization Framework for Fuzzy Inference System Optimization

Gorgin,

Karvandi,

Moghari

et al. 2024

Electronics

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Fuzzy inference systems (FISs) are a key focus for decision-making in embedded systems due to their effectiveness in managing uncertainty and non-linearity. This study demonstrates that optimizing FIS hardware enhances performance, efficiency, and capabilities, improving user experience, heightened productivity, and cost savings. We propose an ultra-low power FIS hardware framework to address power constraints in embedded systems. This framework supports optimizations for conventional arithmetic and Most Significant Digit First (MSDF) computing, ensuring compatibility with MSDF-based sensors. Within the MSDF-computing FIS, fuzzification, inference, and defuzzification processes occur on serially incoming data bits. To illustrate the framework’s efficiency, we implemented it using MATLAB, Chisel3, and Vivado, starting from high-level FIS descriptions and progressing to hardware synthesis. A Scala library in Chisel3 was developed to connect these tools seamlessly, facilitating design space exploration at the arithmetic level. We applied the framework by realizing an FIS for autonomous mobile robot navigation in unknown environments. The synthesis results highlight the superiority of our designs over the MATLAB HDL code generator, achieving a 43% higher clock frequency, and 46% and 67% lower resource and power consumption, respectively.

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Section: Introductionmentioning

confidence: 99%

A Hardware Realization Framework for Fuzzy Inference System Optimization

Gorgin,

Karvandi,

Moghari

et al. 2024

Electronics

Self Cite

View full text Add to dashboard Cite

show abstract

Modeling consistency and consensus in social network group decision making: The role of limited dual tolerance and compromise behaviors

Zhang,

Wang,

Zhu

2024

Applied Soft Computing

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A Hardware Realization Framework for Fuzzy Inference System Optimization

Gorgin,

Karvandi,

Moghari

et al. 2023

Preprint

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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.

show abstract

A fast MILP solver for high-level synthesis based on heuristic model reduction and enhanced branch and bound algorithm

Cited by 3 publications

References 62 publications

A Hardware Realization Framework for Fuzzy Inference System Optimization

A Hardware Realization Framework for Fuzzy Inference System Optimization

Modeling consistency and consensus in social network group decision making: The role of limited dual tolerance and compromise behaviors

A Hardware Realization Framework for Fuzzy Inference System Optimization

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