Results 171 to 180 of about 13,582,659 (253)
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VLSI Design of Saturation-Based Image Dehazing Algorithm
IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 2023Hazy weather degrades the vividness of the images captured by real-time systems for applications such as object detection, remote sensing, and surveillance systems. This affects the performance of such real-time systems.
Bharat Bhushan Upadhyay, K. Sarawadekar
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Machine Learning for VLSI CAD: A Case Study in On-Chip Power Grid Design
IEEE Computer Society Annual Symposium on VLSI, 2021With the improvement of VLSI technology, on-chip power grid design is becoming more challenging than before. In this design phase of VLSI CAD, power grids are generated in order to make power and ground connections to transistors or logic blocks. However,
Sukanta Dey, Sukumar Nandi, G. Trivedi
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Proceedings of the 1985 ACM thirteenth annual conference on Computer Science - CSC '85, 1985
This paper presents a review of the computer-aided design of VLSI devices. For illustrative purposes, the design methodology is modeled as an idealized two-level process. In the first level, known as the functional level, the requirements of the device are converted into logic description and tests are generated.
Vishwani D. Agrawal, Samuel H. C. Poon
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This paper presents a review of the computer-aided design of VLSI devices. For illustrative purposes, the design methodology is modeled as an idealized two-level process. In the first level, known as the functional level, the requirements of the device are converted into logic description and tests are generated.
Vishwani D. Agrawal, Samuel H. C. Poon
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IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2023
The design automation community has been actively exploring machine learning (ML) for very-large-scale-integrated (VLSI) computer-aided design (CAD). Many studies have explored learning-based techniques for cross-stage prediction tasks in the design flow.
Zhuomin Chai +5 more
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The design automation community has been actively exploring machine learning (ML) for very-large-scale-integrated (VLSI) computer-aided design (CAD). Many studies have explored learning-based techniques for cross-stage prediction tasks in the design flow.
Zhuomin Chai +5 more
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Signed Permutation Polytope Packing in VLSI Design
2019 IEEE 15th International Conference on the Experience of Designing and Application of CAD Systems (CADSM), 2019Three mathematical models of a placement problem in VLSI design is offered, where modules are squares, octagons, and rhombuses, and 450-rotation is permitted.
O. Pichugina, Oleksii Kartashov
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Domain-Adapted LLMs for VLSI Design and Verification: A Case Study on Formal Verification
IEEE VLSI Test SymposiumLarge language models (LLMs) present unprecedented opportunities in task automation for industrial chip design and verification that can yield significant improvements in engineering productivity.
Mingjie Liu +4 more
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SpecLLM: Exploring Generation and Review of VLSI Design Specification with Large Language Model
2025 International Symposium of Electronics Design Automation (ISEDA)The development of architecture specifications is an initial and fundamental stage of the integrated circuit (IC) design process. Traditionally, architecture specifications are crafted by experienced chip architects, a process that is not only time ...
Mengming Li +3 more
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Innovative Approaches in Advanced VLSI Design for High-Performance Computing Applications
2024 Second International Conference on Intelligent Cyber Physical Systems and Internet of Things (ICoICI)The increasing demand for high-performance computing (HPC) has driven significant advancements in Very Large Scale Integration (VLSI) design. This paper explores innovative approaches in advanced VLSI design, emphasizing methodologies and technologies ...
K. Sathish +5 more
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Architecture and VLSI design of a VLSI neural signal processor
1993 IEEE International Symposium on Circuits and Systems, 2002A chip based on a scalable parallel systolic VLSI architecture has been designed for executing the compute-bound algorithmic primitives used by search and learning algorithms in neural networks and low level signal processing. The signal processor executes the algorithmic primitives and shared by all neural nets.
Ulrich Ramacher +3 more
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