AI/ML Algorithms and Applications in VLSI Design and Technology
- URL: http://arxiv.org/abs/2202.10015v1
- Date: Mon, 21 Feb 2022 07:01:27 GMT
- Title: AI/ML Algorithms and Applications in VLSI Design and Technology
- Authors: Deepthi Amuru, Harsha V. Vudumula, Pavan K. Cherupally, Sushanth R.
Gurram, Amir Ahmad, Andleeb Zahra, Zia Abbas
- Abstract summary: This paper reviews the AI/ML automated approaches introduced in the past towards VLSI design and manufacturing.
We discuss the scope of AI/ML applications in the future at various abstraction levels to revolutionize the field of VLSI design.
- Score: 3.1171750528972204
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: An evident challenge ahead for the integrated circuit (IC) industry in the
nanometer regime is the investigation and development of methods that can
reduce the design complexity ensuing from growing process variations and
curtail the turnaround time of chip manufacturing. Conventional methodologies
employed for such tasks are largely manual; thus, time-consuming and
resource-intensive. In contrast, the unique learning strategies of artificial
intelligence (AI) provide numerous exciting automated approaches for handling
complex and data-intensive tasks in very-large-scale integration (VLSI) design
and testing. Employing AI and machine learning (ML) algorithms in VLSI design
and manufacturing reduces the time and effort for understanding and processing
the data within and across different abstraction levels via automated learning
algorithms. It, in turn, improves the IC yield and reduces the manufacturing
turnaround time. This paper thoroughly reviews the AI/ML automated approaches
introduced in the past towards VLSI design and manufacturing. Moreover, we
discuss the scope of AI/ML applications in the future at various abstraction
levels to revolutionize the field of VLSI design, aiming for high-speed, highly
intelligent, and efficient implementations.
Related papers
- The Foundations of Computational Management: A Systematic Approach to
Task Automation for the Integration of Artificial Intelligence into Existing
Workflows [55.2480439325792]
This article introduces Computational Management, a systematic approach to task automation.
The article offers three easy step-by-step procedures to begin the process of implementing AI within a workflow.
arXiv Detail & Related papers (2024-02-07T01:45:14Z) - Machine Learning Insides OptVerse AI Solver: Design Principles and
Applications [74.67495900436728]
We present a comprehensive study on the integration of machine learning (ML) techniques into Huawei Cloud's OptVerse AI solver.
We showcase our methods for generating complex SAT and MILP instances utilizing generative models that mirror multifaceted structures of real-world problem.
We detail the incorporation of state-of-the-art parameter tuning algorithms which markedly elevate solver performance.
arXiv Detail & Related papers (2024-01-11T15:02:15Z) - Towards Efficient Generative Large Language Model Serving: A Survey from
Algorithms to Systems [14.355768064425598]
generative large language models (LLMs) stand at the forefront, revolutionizing how we interact with our data.
However, the computational intensity and memory consumption of deploying these models present substantial challenges in terms of serving efficiency.
This survey addresses the imperative need for efficient LLM serving methodologies from a machine learning system (MLSys) research perspective.
arXiv Detail & Related papers (2023-12-23T11:57:53Z) - Evaluating Emerging AI/ML Accelerators: IPU, RDU, and NVIDIA/AMD GPUs [14.397623940689487]
Graphcore Intelligence Processing Unit (IPU), Sambanova Reconfigurable Dataflow Unit (RDU), and enhanced GPU platforms are reviewed.
This research provides a preliminary evaluation and comparison of these commercial AI/ML accelerators.
arXiv Detail & Related papers (2023-11-08T01:06:25Z) - A Survey and Perspective on Artificial Intelligence for Security-Aware
Electronic Design Automation [6.496603310407321]
We summarize the state-of-the-art in AL/ML for circuit design/optimization, security and engineering challenges, research in security-aware CAD/EDA, and future research directions.
arXiv Detail & Related papers (2022-04-19T17:46:39Z) - Enabling Automated Machine Learning for Model-Driven AI Engineering [60.09869520679979]
We propose a novel approach to enable Model-Driven Software Engineering and Model-Driven AI Engineering.
In particular, we support Automated ML, thus assisting software engineers without deep AI knowledge in developing AI-intensive systems.
arXiv Detail & Related papers (2022-03-06T10:12:56Z) - Analog/Mixed-Signal Circuit Synthesis Enabled by the Advancements of
Circuit Architectures and Machine Learning Algorithms [0.0]
We will focus on using neural-network-based surrogate models to expedite the circuit design parameter search and layout iterations.
Lastly, we will demonstrate the rapid synthesis of several AMS circuit examples from specification to silicon prototype, with significantly reduced human intervention.
arXiv Detail & Related papers (2021-12-15T01:47:08Z) - Technology Readiness Levels for Machine Learning Systems [107.56979560568232]
Development and deployment of machine learning systems can be executed easily with modern tools, but the process is typically rushed and means-to-an-end.
We have developed a proven systems engineering approach for machine learning development and deployment.
Our "Machine Learning Technology Readiness Levels" framework defines a principled process to ensure robust, reliable, and responsible systems.
arXiv Detail & Related papers (2021-01-11T15:54:48Z) - AI-based Modeling and Data-driven Evaluation for Smart Manufacturing
Processes [56.65379135797867]
We propose a dynamic algorithm for gaining useful insights about semiconductor manufacturing processes.
We elaborate on the utilization of a Genetic Algorithm and Neural Network to propose an intelligent feature selection algorithm.
arXiv Detail & Related papers (2020-08-29T14:57:53Z) - Technology Readiness Levels for AI & ML [79.22051549519989]
Development of machine learning systems can be executed easily with modern tools, but the process is typically rushed and means-to-an-end.
Engineering systems follow well-defined processes and testing standards to streamline development for high-quality, reliable results.
We propose a proven systems engineering approach for machine learning development and deployment.
arXiv Detail & Related papers (2020-06-21T17:14:34Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.