Machine Learning Techniques for VLSI Chip Design E-Book

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Sprache: Englisch

Seitenzahl: 290

Veröffentlichungsjahr: 2023

Beschreibung

MACHINE LEARNING TECHNIQUES FOR VLSI CHIP DESIGN This cutting-edge new volume covers the hardware architecture implementation, the software implementation approach, the efficient hardware of machine learning applications with FPGA or CMOS circuits, and many other aspects and applications of machine learning techniques for VLSI chip design. Artificial intelligence (AI) and machine learning (ML) have, or will have, an impact on almost every aspect of our lives and every device that we own. AI has benefitted every industry in terms of computational speeds, accurate decision prediction, efficient machine learning (ML), and deep learning (DL) algorithms. The VLSI industry uses the electronic design automation tool (EDA), and the integration with ML helps in reducing design time and cost of production. Finding defects, bugs, and hardware Trojans in the design with ML or DL can save losses during production. Constraints to ML-DL arise when having to deal with a large set of training datasets. This book covers the learning algorithm for floor planning, routing, mask fabrication, and implementation of the computational architecture for ML-DL. The future aspect of the ML-DL algorithm is to be available in the format of an integrated circuit (IC). A user can upgrade to the new algorithm by replacing an IC. This new book mainly deals with the adaption of computation blocks like hardware accelerators and novel nano-material for them based upon their application and to create a smart solution. This exciting new volume is an invaluable reference for beginners as well as engineers, scientists, researchers, and other professionals working in the area of VLSI architecture development.

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Machine Learning for VLSI Chip Design

Edited by

Abhishek KumarSuman Lata TripathiandK. Srinivasa Rao

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Library of Congress Cataloging-in-Publication Data

ISBN 9781119910398

Front cover images supplied by Pixabay.comCover design by Russell Richardson

List of Contributors

Imran Ullah KhanDept. of Electronics and Communication Engineering, Integral University, Lucknow, India

Nupur MittalDept. of Electronics and Communication Engineering, Integral University, Lucknow, India

Mohd. Amir AnsariDept. of Electronics and Communication Engineering, Integral University, Lucknow, India

A.V. Ananthalakshmi Department of ECE, Puducherry Technological University, Puducherry, India

P. DivyaparameswariDepartment of ECE, Puducherry Technological University, Puducherry, India

P. KanimozhiDepartment of ECE, Puducherry Technological University, Puducherry, India

Jyoti KandpalDept. of Electronics and Communication Engineering, NIT Arunanchal Pradesh, India

RajeswariDepartment of ECE, Lakireddy Bali Reddy College of Engineering, Mylavaram, India

N. Vinod KumarDepartment of ECE, Lakireddy Bali Reddy College of Engineering, Mylavaram, India

K. M. SureshDepartment of ECE, Lakireddy Bali Reddy College of Engineering, Mylavaram, India

N. Sai KumarDepartment of ECE, Lakireddy Bali Reddy College of Engineering, Mylavaram, India

K. Girija SravaniDepartment of ECE, KL University, Green Fields, Guntur-522502, Andhra Pradesh, India

P. Kiran KumarKoneru Lakshmaiah Educational Foundation (Deemed to be University), Guntur, Andhra Pradesh-522502, India

B. BalajiKoneru Lakshmaiah Educational Foundation (Deemed to be University), Guntur, Andhra Pradesh-522502, India

M. SumanKoneru Lakshmaiah Educational Foundation (Deemed to be University), Guntur, Andhra Pradesh-522502, India

P. Syam SundarKoneru Lakshmaiah Educational Foundation (Deemed to be University), Guntur, Andhra Pradesh-522502, India

E. PadmajaKoneru Lakshmaiah Educational Foundation (Deemed to be University), Guntur, Andhra Pradesh-522502, India

Ritu YadavECE Department, I K Gujaral Punjab Technical University, Jalandhar, India

Kiran AhujaECE Department, DAVIET, Jalandhar, India

K. Sasi BhushanDepartment of Electronics and Communication Engineering, Lakireddy Bali Reddy College of Engineering (Autonomous), Mylavaram, Krishna District, AP, India, 521230

U. PreethiDepartment of Electronics and Communication Engineering, Lakireddy Bali Reddy College of Engineering (Autonomous), Mylavaram, Krishna District, AP, India, 521230

P. Naga Sai NavyaDepartment of Electronics and Communication Engineering, Lakireddy Bali Reddy College of Engineering (Autonomous), Mylavaram, Krishna District, AP, India, 521230

R. AbhilashDepartment of Electronics and Communication Engineering, Lakireddy Bali Reddy College of Engineering (Autonomous), Mylavaram, Krishna District, AP, India, 521230

T. PavanDepartment of Electronics and Communication Engineering, Lakireddy Bali Reddy College of Engineering (Autonomous), Mylavaram, Krishna District, AP, India, 521230

B. Ramesh ReddyDepartment of Electronics and Communication Engineering, LBR College of Engineering, Mylavaram, Krishna District, Andhra Pradesh

K. Bhargav ManikantaDepartment of Electronics and Communication Engineering, LBR College of Engineering, Mylavaram, Krishna District, Andhra Pradesh

P.V.V.N.S. Jaya SaiDepartment of Electronics and Communication Engineering, LBR College of Engineering, Mylavaram, Krishna District, Andhra Pradesh

R. Mohan ChandraDepartment of Electronics and Communication Engineering, LBR College of Engineering, Mylavaram, Krishna District, Andhra Pradesh

M. Greeshma VyasDepartment of Electronics and Communication Engineering, LBR College of Engineering, Mylavaram, Krishna District, Andhra Pradesh

B. V. Anil Sai KumarSchool of Electronics and Electrical Engineering, Lovely Professional University, Punjab, India

Suryavamsham Prem KumarSchool of Electronics and Electrical Engineering, Lovely Professional University, Punjab, India

Konduru JaswanthSchool of Electronics and Electrical Engineering, Lovely Professional University, Punjab, India

Kola VishnuSchool of Electronics and Electrical Engineering, Lovely Professional University, Punjab, India

Abhishek KumarSchool of Electronics and Electrical Engineering, Lovely Professional University, Punjab, India

Narendra Babu AlurDepartment of Electronics and Communication Engineering, and Engineering, Lakireddy Bali Reddy College of Engineering (Autonomous), Mylavaram, Krishna District, AP, India

Kurapati Poorna DurgaDepartment of Electronics and Communication Engineering, and Engineering, Lakireddy Bali Reddy College of Engineering (Autonomous), Mylavaram, Krishna District, AP, India

Boddu GaneshDepartment of Electronics and Communication Engineering, and Engineering, Lakireddy Bali Reddy College of Engineering (Autonomous), Mylavaram, Krishna District, AP, India

Manda DevakarunaDepartment of Electronics and Communication Engineering, and Engineering, Lakireddy Bali Reddy College of Engineering (Autonomous), Mylavaram, Krishna District, AP, India

Lakkimsetti NandiniDepartment of Electronics and Communication Engineering, and Engineering, Lakireddy Bali Reddy College of Engineering (Autonomous), Mylavaram, Krishna District, AP, India

A. PraneethaDepartment of Computer Science Engineering, Lakireddy Bali Reddy College of Engineering (Autonomous), Mylavaram, Krishna District, AP, India

T. SatyanarayanaDepartment of Electronics and Communication Engineering, and Engineering, Lakireddy Bali Reddy College of Engineering (Autonomous), Mylavaram, Krishna District, AP, India

K. Rani RudramaDepartment of Electronics and Communication Engineering, Lakireddy Bali Reddy College of Engineering (Autonomous), Mylavaram, Krishna District, AP, India

Mounika RamalaDepartment of Electronics and Communication Engineering, Lakireddy Bali Reddy College of Engineering (Autonomous), Mylavaram, Krishna District, AP, India

Poorna sasank GalapartiDepartment of Electronics and Communication Engineering, Lakireddy Bali Reddy College of Engineering (Autonomous), Mylavaram, Krishna District, AP, India

Manikanta Chary DarlaDepartment of Electronics and Communication Engineering, Lakireddy Bali Reddy College of Engineering (Autonomous), Mylavaram, Krishna District, AP, India

Siva Sai Prasad LoyaDepartment of Electronics and Communication Engineering, Lakireddy Bali Reddy College of Engineering (Autonomous), Mylavaram, Krishna District, AP, India

K. Srinivasa RaoDepartment of Electronics and Communication Engineering, KLEF, Vaddeswaram, Green Fields, 522502, Andhra Pradesh, India

T. Anil RajuDepartment of Electronics and Communication Engineering, Lakireddy Bali Reddy College of Engineering, Mylavaram

K. Srihari ReddyDepartment of Electronics and Communication Engineering, Lakireddy Bali Reddy College of Engineering, Mylavaram

Sk. Arifulla RabbaniDepartment of Electronics and Communication Engineering, Lakireddy Bali Reddy College of Engineering, Mylavaram

G. SureshDepartment of Electronics and Communication Engineering, Lakireddy Bali Reddy College of Engineering, Mylavaram

K. Saikumar ReddyDepartment of Electronics and Communication Engineering, Lakireddy Bali Reddy College of Engineering, Mylavaram

Rajesh C. DharmikDepartment of Information Technology, Yeshwantrao Chavan College of Engineering, Nagpur

Bhushan U. BawankarDepartment of Information Technology, Yeshwantrao Chavan College of Engineering, Nagpur

Preface

Machine Learning (ML) has touched all corners of human life and industry. Databased learning intelligence supports are the scalability of present technology and architecture. The current ML and deep learning (DL) algorithms require huge consumption of data and power. The industry is looking for an efficient VLSI circuit that can meet the demands of the AI-ML-DL universe. ML can pioneer different sectors throughout design methodologies from RTL design, synthesis, and verification. One of the deepest challenges of chip design is the time-consuming iterative process. Thanks to the learning model, time is considerably reduced. VLSI-based solutions and innovation of AI-ML-DL applications are growing in demand. Internet of Things–based solutions address the various challenges in society that require chips. This new book covers the latest AI/ML techniques, VLSI chip design, and systems to address societal challenges.

1Applications of VLSI Design in Artificial Intelligence and Machine Learning

Imran Ullah Khan, Nupur Mittal* and Mohd. Amir Ansari

Dept. of Electronics and Communication Engineering, Integral University, Lucknow, India

Abstract

In our advanced times, complementary metal-oxide semiconductor (CMOS) based organizations like semiconductor and gadgets face extreme scheduling of products and other different pressures. For resolving this issue, electronic design automation (EDA) must provide “design-based equivalent scaling” to continue the critical industry trajectory. For solving this problem machine learning techniques should be used both inside and “peripherally” in the design tools and flows. This article reviews machine learning opportunities, and physical implementation of IC will also be discussed. Cloud intelligence-enabled machine learning-based data analytics has surpassed the scalability of current computing technologies and architectures. The current methods based on deep learning are inefficient, require a lot of data and power consumption, and run on a data server with a long delay. With the advent of self-driving cars, unmanned aerial vehicles and robotics, there is a huge need to analyze only the necessary sensory data with low latency and low power consumption on edge devices. In this discussion, we will talk about effective AI calculations, for example, fast least squares, binary and tensor convolutional neural organization techniques, and compare prototype accelerators created in field preogrammable gate array (FPGA) and CMOS-ASIC chips. Planning on future resistive random access memory (RRAM) gadgets will likewise be briefly depicted.

Keywords: VLSI, AI, ML, CAD & AVM

1.1 Introduction

Rapid growth in IC technology is catching up with IC design capabilities, mainly due to the significant advancement in artificial intelligence. The computational tasks assigned to very large-scale integration (VLSI) are time-consuming processes but when AI is implemented to perform the same computational tasks, the required time will be reduced. As technology advances rapidly, VLSI developers must observe and implement this growth to augment design tools. Improved design methods, features, and capabilities bring the promise of AI to VLSI design. Although artificial intelligence brings many features and methods, it still has certain limitations to bring solutions to various problems. As a result, the advent of machine learning (ML) opens up a slew of new possibilities for collaboration and particular sectors of VLSI and computer-based design. By using AI, chips are designed and implemented. It is seen as the premier application of artificial intelligence. Currently, computer-based design tools are commonly utilised in conjunction with information learned from introductory AI classes. Previously, chips were mostly hand-designed, the chip size was too large, and the performance was slow. Validating those chips based on hand-designed designs is a complex task. These complexities lead to the development of automated tools. The automation tool has been upgraded for other tasks assigned to it. Researchers bring new design methods from time to time, such as memory combinations, new programs in computing tasks, etc., in the design process, which must be mechanised. For these objectives, companies such as Intel, IBM, and others have in-house computer-aided design (CAD) capabilities [1–4]. Cadence, Synopsys, Mentor Graphics, and a slew of other companies sell CAD software, which can be thought of as artificial intelligence applied to chip design. For identifying patterns, documents retrieved or gathered from clusters is sometimes required. Such patterns can be detected by concentrating on things like classifying diverse items, forecasting points of interest, input-output linkages based on their complexity, and deep neural networks with numerous other layers for each pattern, object, and speech recognition application. In the domains mentioned above, technology is of tremendous importance. DNNs must respond to new information by comparing it to previously proposed information or procedures. This has to be expanded to the most recent development level. If the system is non-stationary, the decision-making process must be tweaked in order to enhance the increasing efficiency, which is a result of machine learning [5, 6].

In former times, huge computers made up of large-size vaccum tubes were used. Even though they were heralded as the world’s fastest computers at the time, they were no match for current machines. With each passing second, modern computers become smaller, faster, cheaper, more powerful, and more efficient. But what is causing this shift? With the introduction of Bardeen’s (1947–48) semiconductor transistor and Shockley’s (1949) bipolar transistor at Bell Labs, the entire computing field entered a new era of electronic downsizing. The development span of microelectronics is shorter than the average human lifespan, but it has seen as many as four generations. Small-scale integration (SSI) was a term used in the early 1960s to describe low-density manufacturing procedures in which the number of transistors was restricted to roughly ten.

In the late 1960s, this gave way to Medium-Scale Integration (MSI), which allowed for the placement of roughly 100 transistors on a single chip. The Transistor-Transistor Logic (TTL), which provided higher integration densities, outlasted other IC families’ Emitter-Coupled Logic (ECL) and established the foundation for integrated circuit uprising. Texas Instruments, Fairchild, Motorola, and National Semiconductor all trace their roots back to the establishment of this family. The development of transistor counts to roughly 1,000 per chip, known as large-scale integration, began in the early 1970s (LSI). On a single chip the number of transistors had surpassed 1,000 by the mid-1980s, ushering in the era of very high-scale integration (VLSI). Despite the fact that significant advances have been achieved and transistor counts have increased, TTL was vanquished in the struggle against the MOS at this time, due to the same concerns that put the vacuum tube out of commission: power consumption and the number of gates that could be placed on a single die. With the introduction of the microprocessors, Intel’s 4004 in 1972 and the 8080 in 1974, the second period of the integrated circuit revolution began. Texas Instruments, Infineon, Alliance Semiconductors, Cadence, Synopsys, Cisco, Micron, National Semiconductor, STMicroelectronics, Qualcomm, Lucent, Mentor Graphics, Analog Devices, Intel, Philips, Motorola, and many others are among the firms that make semiconductors today. Many aspects of VLSI have been demonstrated and committed to, including programmable logic devices (PLDs), hardware languages, design tools, embedded systems, and so on. As an example, the creation of an artificial neural network necessitates the use of several neural hubs as well as various amplifiers stages. With an increase in the number of neural hubs, a larger area is required to position such nodes, and the number of neural node interdependencies in diverse layers appears to be modest. It complicates cell networking in a small chip zone; therefore big area specifications for speakers and storage devices limit the device’s volume. Due to the device’s unpredictable nature, using a fuzzy logic chip with a large number of information sources is impractical.

1.2 Artificial Intelligence

Artificial intelligence is a branch of computer emphasis on invention of technology that can engage in intelligent actions. Humans have been fascinated by the ability to construct sentient robots since ancient times, and today, thanks to the introduction of computers and 50 years of scientific research into machine intelligence development tools, that dream is becoming a reality. Researchers are developing computers that can think like humans, interpret speech, defeat human chess grandmasters, and perform a slew of other previously unimaginable tasks [2].

1.3 Artificial Intelligence & VLSI (AI and VLSI)

The field of expert systems functioning as design assistants is where artificial intelligence (AI) is thriving in silicon chip and printed circuit design schematics [3, 9]. However, AI is simply one facet of expert technology. VLSI designing is a difficult task. That complexity is also multi-dimensional. Self-design and the patterned origin of the construction process are two others. AI language aids in the resolution of such difficult issues. These language properties, when joined with intelligent systems, enable a critical first step in addressing extremely difficult issues, notably confirming the design’s validity [3].

1.4 Applications of AI

Uses of AI are developing quickly. These are being sought after in college research as well as in modern conditions like in industries. The field of VLSI design is adapting AI rapidly [7, 8, 11]. The first important application is the expert system, an intelligent computer software that mimics the behaviour of a human by employing analytical techniques to a specific domain’s knowledge base. Expert systems in the professional field should be capable of resolving instant and reasonably challenging situations. Each difficulty should have one or even more solutions provided by an expert system. These alternatives should be reliable. Expert systems differ from regular computer programs in several important ways. “Intelligence” is specifically written into the code of traditional computer programmes. The code subsequently fixes the issue by using a well-known algorithm to do so. The “intelligence” part of expert systems is distinct from the controlling or reasoning part. Modification and improvements to the learning can be made without affecting the control portion [4]. The key aspect of artificial intelligence’s knowledge-based techniques is that they ask human specialists what knowledge they use to solve certain tasks and then design multiple algorithms formats that can directly express that information. Researchers that have used this technology in a variety of VLSI applications have seen some advantages over simpler methods., such as those discussed in [4].

Making incremental improvements will be easier by using this method and it is easier for the system to describe what it is doing and why. For human experts it is easy to identify where the system’s knowledge is incorrect or incomplete and describe how to solve it. It is easier to interact with human professionals’ abilities.

In VLSI design these expert system are being used widely [7, 8, 10, 12]. Design Automation Assistant (DAA) was one of the first expertise solutions for VLSI design. In VLSI, it is very crucial. Researchers from Carnegie-Mellon University and AT&T Bell Labs collaborated to create it. The original DAA had rules describing several synthesis activities. Registers, operators, data routes, and control signals were used to represent production rules. Over the years, the DAA technology has been continually improved and expanded [3]. Its database contains over 500 rules that are utilised in the construction of various systems. NCR’s Design Advisor serves as a professional help. The design advisor’s job is to offer guidance in six areas for the creation of semi-custom CMOS devices using a library of functional blocks. Simulations, functions, timing, testability, design rules, and specification feasibility are all covered in the advisor.

1.5 Machine Learning

Advanced systems are being used and developed that are capable of learning and adapting without explicit instructions by analysing and drawing inferences from data patterns utilising specific algorithms models [13]. Machine learning also includes Artificial intelligence. Machine learning covers a vast area in medicine, email filtering, speech recognition, and computer vision. For many uses, developing traditional algo is not possible. The solution is machine learning [14–16]. The use of machine learning in biological datasets is on the rise.

Computation analytics, which emphasizes the use of computers to generate predictions, is closely related to machine learning; however, not all algorithms are statistical learning. Unsupervised learning is the focus of data mining, which is a similar topic of research. Biological brains is also a very important application of machine learning [

17

,

18

].

1.6 Applications of ML

1.6.1 Role of ML in Manufacturing Process

A manufacturer can gain actual benefits with the use of ML, such as increased efficiency and lower costs. Machine learning can be used to improve the industry sector. In the case of Google, the company reduced its data center electricity usage by 40% by using custom ML. Google also tried to reduce it manually but that improvement was not acheived. Many other companies adopted ML. Using machine learning to improve internal operational efficiency, more than 80% say it helps them reduce costs.

1.6.2 Reducing Maintenance Costs and Improving Reliability

Machine learning can be used to create optimized maintenance schedules based on actual equipment usage. In the same way, customers will also benefit, since they can be offered personalized maintenance plans. Using machine learning to more accurately predict customer demand, a textile manufacturer was able to reduce inventory levels by 30%. By using ML, inventory levels and waste can also be reduced.

Figure 1.1 Machine learning in process industries.

1.6.3 Enhancing New Design

With the help of ML, the consumer exactly knows the application of the product. If the product fails, anyone can know the reason behind it. These problems can be fed back to the team, which will remove all the problems with the help of machine learning. By using ML researchers can enhance their R&D capabilities. Figure 1.1 shows the hierarchical applications of data analytics and machine learning in process industries.

1.7 Role of ML in Mask Synthesis

Various resolution enhancement techniques (RET), such as optical proximity correction (OPC), source mask co-optimization, and sub-resolution assist functions (SRAF), become necessary as technological nodes reach the limits of optical wavelengths. Machine learning will be used by various RETs to improve mask synthesis turnaround time.

Figure 1.2