VLSI implementation of coupled MRF model using pulse‐coupled phase oscillators

As the demand for artificial intelligence continues to grow, there is a need for specialized hardware that can efficiently process and power AI algorithms. This is where VLSI (Very Large-Scale Integration) design comes into play. By integrating millions, or even billions, of transistors onto a single chip, VLSI design enables the creation of powerful AI-integrated circuits. However, designing hardware specifically for artificial intelligence poses its own set of challenges. The complexity of AI algorithms and the need for parallel processing capabilities demand innovative solutions. VLSI designers must carefully consider factors such as power consumption, heat dissipation, and the integration of specialized neural network accelerators. In addition to these considerations, VLSI designers must also address the scaling limitations of semiconductor technology. As AI algorithms become more complex, the demand for advanced VLSI architecture increases. Designers must find ways to enhance transistor density and performance while overcoming the limitations imposed by the physical laws governing semiconductor manufacturing. In-Memory Computing (IMC) is an emerging paradigm designed to overcome the limitations of the traditional Von Neumann architecture, particularly for energy-efficient AI applications. Traditional computing systems suffer from the "memory wall"—a bottleneck caused by the constant transfer of data between the CPU and memory. This leads to inefficiencies in latency, bandwidth, and energy consumption, especially for AI tasks which involve huge volumes of matrix operations and data movement. Keywords: Very Large-Scale Integration (VLSI), Artificial Intelligence (AI), In-memory computing (IMC), Neuromorphic, multiply-and-accumulate (MAC), RRAM (Resistive RAM), MRAM (Magnetoresistive RAM), PCM (Phase-Change Memory), FeFET (Ferroelectric FET), spiking neural networks (SNNs)

The Cascaded Integrator-Comb (CIC) filter is a digital filter used principally in the multi-rate process of signal processing, particularly in decimation and interpolation. Its rudimentary architecture, free of multipliers, allows its implementation in very large scale integration (VLSI) with reduced chip area and low dissipation, which are important considerations for VLSI design. One of the strengths of this filter's cascaded integrator and comb stages is that high decimation or interpolation factors are handled with low complexity. In VLSI, digital filter design mainly involves optimizing parameters like passband, stopband, and attenuation under constrained resources of speed, power, and silicon area. VLSI-based digital systems find CIC filters very useful for their applications, as they provide high-speed data processing with very minimal hardware, which is critical for current signal processing.

Latch-up in CMOS (Complementary Metal-Oxide-Semiconductor) technology is a significant reliability concern in VLSI (Very Large Scale Integration) design. Latch-up is a parasitic, unintended creation of a low-impedance path between the power supply rails due to the triggering of parasitic thyristor structures inherent in CMOS processes. This phenomenon can lead to device malfunction, excessive current flow, and potential permanent damage to the integrated circuit. The impact of latch-up is profound, as it compromises the stability and functionality of electronic systems, especially in high-performance and high-reliability applications such as aerospace, medical devices, and critical infrastructure. The primary factors contributing to latch-up include high current spikes, radiation exposure, and improper circuit layout. Preventive measures are essential to mitigate these risks and ensure the robustness of CMOS circuits. Effective prevention strategies include the use of guard rings, which isolate the sensitive regions of the chip and prevent the formation of parasitic paths. Proper substrate doping and well design can also minimize latch-up susceptibility. Additionally, optimizing the layout design to minimize the proximity of p-n junctions and employing advanced CMOS processes with latch-up resistant technologies are crucial. This abstract highlights the importance of understanding and preventing latch-up in CMOS VLSI design to enhance the reliability and performance of modern integrated circuits.

The application of Machine Learning (ML) in Electronic Design Automation (EDA) for Very Large-Scale Integration (VLSI) design has garnered significant research attention. Despite the requirement for extensive datasets to build effective ML models, most studies are limited to smaller, internally generated datasets due to the lack of comprehensive public resources. In response, we introduce EDALearn, the first holistic, open-source benchmark suite specifically for ML tasks in EDA. This benchmark suite presents an end-to-end flow from synthesis to physical implementation, enriching data collection across various stages. It fosters reproducibility and promotes research into ML transferability across different technology nodes. Accommodating a wide range of VLSI design instances and sizes, our benchmark aptly represents the complexity of contemporary VLSI designs. Additionally, we provide an in-depth data analysis, enabling users to fully comprehend the attributes and distribution of our data, which is essential for creating efficient ML models. Our contributions aim to encourage further advances in the ML-EDA domain.

The trend in design and manufacturing of very large-scale integrated (VLSI) circuits is towards smaller devices on increasing wafer dimensions. VLSI is the inter-disciplinary science of the process of creating an integrated circuit (IC) by combining thousands of transistors into a single chip. VLSI design can reduce the area of the circuit, making it less expensive and requiring less power.The book gives an understanding of the underlying principles of the subject. It not only focuses on circuit design process obeying VLSI rules but also on technological aspects of prototyping and fabrication. All the clocking processes, interconnects, and circuits of CMOS are explained in this book in an understandable format. The book provides contents on VLSI Physical Design Automation, Design of VLSI Devices and also its Impact on Physical Design.The book is intended as a reference book for senior undergraduate, first-year post graduate students, researchers as well as academicians in VLSI design, electronics & electrical engineering, and materials science. The basics and applications of VLSI design from STA, PDA and VLSI Testing along with FPGA based Prototyping are covered in a comprehensive manner.The latest technology used in VLSI design is discussed along with the available tools for FPGA prototyping as well as ASIC design. Each unit contains technical questions with solutions at the end.Technical topics discussed in the book include: • Static Timing Analysis• CMOS Layout and Design rules• Physical Design Automation• Testing of VLSI Circuits• Software tools for Frontend and Backend design.

The fast-moving telecommunications industry is driving the demand for Very Large Scale Integration (VLSI) design approaches that can achieve higher performance at lower power consumption and higher reliability. This paper presents enhanced VLSI design methodologies tailored for telecommunications systems, with an emphasis on key performance metrics optimization, such as speed, power efficiency, and scalability. The new strategies presented incorporate novel design paradigms including power-aware architectures, adaptive clocking mechanisms, and high-density integration techniques. Additionally, deep sub-micron technology has brought with it a whole new set of challenges, such as increased leakage currents and signal integrity problems, which are mitigated through novel circuit-level techniques and layout optimizations. One of the major focuses in the research is hardware integration, especially the accelerators needed for specialized functions in telecommunications: signal processing and encryption. Next, design methodologies supported by innovative CAD tools and simulation environments have been discussed; this helps increase design efficiency as well as helps reduce time-to-market. To that end, low-power techniques such as multi-threshold voltage and power gating have been focused on in relation to the design of energy-efficient hardware for telecommunications. This paper demonstrates, through simulation results and case studies, that the proposed VLSI strategies can improve system performance significantly while maintaining power efficiency and low cost. The findings contribute to the ongoing research and development in VLSI technology and offer a roadmap for designing next-generation telecommunications systems capable of supporting emerging applications such as 5G, IoT, and beyond. These strategies not only enhance the robustness of the design but also open the way toward more scalable and adaptive telecommunications infrastructures

A compact neural network algorithm for partial-response maximum-likelihood (PRML) sequence detection is presented. Compact neural networks are a class of locally connected neural networks suitable for very large scale integration (VLSI) implementation. The hardware complexity for VLSI implementation of the proposed algorithm grows linearly with the level of the deliberately designed symbol interference effects of the partial-response (PR) signalling scheme. Large dedicated memory for storage of likelihood matrices in digital Viterbi-algorithm-based detectors is not needed for the proposed detector. Detailed analysis on network stability for network topology and time constant of an analog neuron is described. This detector algorithm has competitive bit-error rate performance when compared with the digital Viterbi algorithm under the noise condition for many real-world applications. The proposed algorithm is suitable for analog VLSI implementation because of its low time complexity and linear area complexity for the detection of PRML signalling schemes.

Device scaling is an important part of the very large scale integration (VLSI) design to boost up the success path of VLSI industry, which results in denser and faster integration of the devices. As technology node moves towards the very deep submicron region, leakage current and circuit reliability become the key issues. Both are increasing with the new technology generation and affecting the performance of the overall logic circuit. The VLSI designers must keep the balance in power dissipation and the circuit's performance with scaling of the devices. In this paper, different scaling methods are studied first. These scaling methods are used to identify the effects of those scaling methods on the power dissipation and propagation delay of the CMOS buffer circuit. For mitigating the power dissipation in scaled devices, we have proposed a reliable leakage reduction low power transmission gate (LPTG) approach and tested it on complementary metal oxide semiconductor (CMOS) buffer circuit. All simulation results are taken on HSPICE tool with Berkeley predictive technology model (BPTM) BSIM4 bulk CMOS files. The LPTG CMOS buffer reduces 95.16% power dissipation with 84.20% improvement in figure of merit at 32 nm technology node. Various process, voltage and temperature variations are analyzed for proving the robustness of the proposed approach. Leakage current uncertainty decreases from 0.91 to 0.43 in the CMOS buffer circuit that causes large circuit reliability.

The rapid increase in the complexity of Very Large-Scale Integration (VLSI) systems, along with continuous semiconductor scaling, has made conventional design and automation techniques increasingly time-consuming and resource-intensive. AI-driven VLSI design and automation have emerged as a powerful paradigm that applies machine learning, deep learning, and data-driven optimization to improve the efficiency, accuracy, and scalability of Electronic Design Automation (EDA) tools. By learning from large volumes of design data, AI techniques enable intelligent decision-making across key stages of the VLSI design flow, including architecture exploration, logic synthesis, placement and routing, timing analysis, power optimization, and verification. This chapter provides an overview of AI-based methodologies used in modern VLSI automation, emphasizing predictive modeling and reinforcement learning for faster design cycles and improved performance, power, and area (PPA) metrics. It also discusses challenges such as data quality, model interpretability, and tool integration, highlighting AI’s critical role in next-generation VLSI systems.

Very Large-Scale Integration (VLSI) and semiconductor technology form the foundation of modern electronic systems, enabling the realization of complex, high-performance, and low-power integrated circuits. This chapter presents a comprehensive overview of the fundamental concepts underlying VLSI and semiconductor technology, beginning with the basic principles of semiconductor physics and device operation. Key topics such as PN junctions, MOSFET structures, CMOS technology, and fabrication processes are introduced to provide a clear understanding of how electronic components are realized at the chip level. The chapter also discusses the evolution of integrated circuits, scaling trends, and the impact of Moore’s law on device performance, power consumption, and reliability. Emphasis is placed on the interrelationship between materials, devices, and circuit design, highlighting how technological constraints influence VLSI architectures and system performance. By establishing a strong conceptual foundation, this chapter prepares readers for advanced topics in VLSI design, fabrication, and emerging semiconductor technologies, serving as a fundamental reference for students and professionals alike.

This paper presents an in-depth study on utilizing machine learning techniques to predict power consumption in Very Large Scale Integration (VLSI) circuits. By leveraging historical design data, we develop and validate predictive models that aid designers in making power-efficient decisions early in the design process. Our results show significant improvements in prediction accuracy, highlighting promising directions for future VLSI design optimization.

Low-power microarchitecture designs of Viterbi decoder are considered one of the on-demanding issues in Very Large-Scale Integration (VLSI). Viterbi decoders consume 35% of the power and 76% of the complexity in digital communication schemes, e.g., IEEE 802.11 a/g WLAN schemes. Development of Viterbi decoder itself reduces dramatically system complexity; the decoder has a source of power sink unit which rises in Add Compare-Select (ACS) unit. Various levels of development exist in VLSI such as architecture, microarchitecture, circuit, and transistor. ACS is structured of adder, comparator, and selector, so microarchitecture development of ACS unit intends one of these superior blocks. Adders in VLSI designs have a tremendous interest as they are the kernel of any digital system so enhancing this basic building block, which is used over and over, influences system specification. This paper targets different microarchitectural approaches to shrink the power of this unit which enhances overall system specification. Microarchitecture development encompasses using another block instead of the existing one or repositioning current blocks. In this review paper, comparisons of different parallel adders are presented to achieve low consumption power while obtaining high-speed operation of the addition, and an arrangement of ACS blocks to reduce the complexity of the decoder is also provided.

VLSI and intelligent transducers

Very Large-Scale Integration (VLSI) technology has been crucial to the rapid advancement of contemporary electronics because it has made it possible for small, fast, and energy-efficient systems to be employed in artificial intelligence, biomedical devices, communication, and computation. Innovation in devices from the micrometer scale to the present nanoscale and new atomic-scale technologies has been fueled by ongoing improvements in materials, fabrication techniques, system architectures, and design methodology. This paper offers a thorough summary of recent developments in VLSI design and technology, with particular attention on heterogeneous chiplet-based technologies, low-power architecture, high-performance computing systems, hardware that is neuromorphic and AI-oriented, and 3D integrated circuits. A thorough discussion is held regarding the main issues of manufacturing complexity, power management, thermal reliability, and scaling. Future themes are highlighted in the assessment, including spintronics, quantum computing, AI-driven design automation, 3D/2.5D integration, beyond-CMOS devices, and sustainable semi-conductor technologies.

Blind equalization for time-varying channels and multiple samples processing using particle filtering