On-chip Voltage Regulator– Circuit Design and Automation

Title from PDF of title page viewed May 24, 2021Dissertation advisors: Masud H Chowdhury and Yugyung LeeVitaIncludes bibliographical references (page 106-121)Thesis (Ph.D.)--School of Computing and Engineering. University of Missouri--Kansas City, 2021With the increase of density and complexity of high-performance integrated circuits and systems, including many-core chips and system-on-chip (SoC), it is becoming difficult to meet the power delivery and regulation requirements with off-chip regulators. The off-chip regulators become a less attractive choice because of the higher overheads and complexity imposed by the additional wires, pins, and pads. The increased I2R loss makes it challenging to maintain the integrity of different voltage domains under a lower supply voltage environment in the smaller technology nodes. Fully integrated on-chip voltage regulators have proven to be an effective solution to mitigate power delivery and integrity issues. Two types of regulators are considered as most promising for on-chip implementation: (i) the low-drop-out (LDO) regulator and (ii) the switched-capacitor (SC)regulator. The first part of our research mainly focused on the LDO regulator. Inspired by the recent surge of interest for cap-less voltage regulators, we presented two fully on-chip external capacitor-less low-dropout voltage regulator design. The second part of this proposal explores the complexity of designing each block of the regulator/analog circuit and proposed a design methodology for analog circuit synthesis using simulation and learning-based approach. As the complexity is increasing day-by-day in an analog circuit, hierarchical flow mostly uses for design automation. In this work, we focused mainly on Circuit-level, one of the significant steps in the flow. We presented a novel, efficient circuit synthesis flow based on simulation and learning-based optimization methods. The proposed methodology has two phases: the learning phase and the evaluation phase. Random forest, a supervised learning is used to reduce the sample points in the design space and iteration number during the learning phase. Additionally, symmetric constraints are used further to reduce the iteration number during the sizing process. We introduced a three-step circuit synthesis flow to automate the analog circuit design. We used H-spice as a simulation tool during the evaluation phase of the proposed methodology. The three most common analog circuits are chosen: single-stage differential amplifier, operational transconductance amplifier, and two-stage differential amplifier to verify the algorithm. The tool is developed in Python, and the technology we used is0.6um. We also verified the optimized result in Cadence Virtuoso.Introduction -- On-chip power delivery system -- Fundamentals of on-chip voltage regulator -- LDO design in 45NM technology -- LDO design in technology -- Analog design automation -- Proposed analog design methodology -- Energy efficient FDSOI and FINFET based power gating circuit using data retention transistor -- Conclusion and future wor

Efficient and Scalable Computing for Resource-Constrained Cyber-Physical Systems: A Layered Approach

With the evolution of computing and communication technology, cyber-physical systems such as self-driving cars, unmanned aerial vehicles, and mobile cognitive robots are achieving increasing levels of multifunctionality and miniaturization, enabling them to execute versatile tasks in a resource-constrained environment. Therefore, the computing systems that power these resource-constrained cyber-physical systems (RCCPSs) have to achieve high efficiency and scalability. First of all, given a fixed amount of onboard energy, these computing systems should not only be power-efficient but also exhibit sufficiently high performance to gracefully handle complex algorithms for learning-based perception and AI-driven decision-making. Meanwhile, scalability requires that the current computing system and its components can be extended both horizontally, with more resources, and vertically, with emerging advanced technology. To achieve efficient and scalable computing systems in RCCPSs, my research broadly investigates a set of techniques and solutions via a bottom-up layered approach. This layered approach leverages the characteristics of each system layer (e.g., the circuit, architecture, and operating system layers) and their interactions to discover and explore the optimal system tradeoffs among performance, efficiency, and scalability. At the circuit layer, we investigate the benefits of novel power delivery and management schemes enabled by integrated voltage regulators (IVRs). Then, between the circuit and microarchitecture/architecture layers, we present a voltage-stacked power delivery system that offers best-in-class power delivery efficiency for many-core systems. After this, using Graphics Processing Units (GPUs) as a case study, we develop a real-time resource scheduling framework at the architecture and operating system layers for heterogeneous computing platforms with guaranteed task deadlines. Finally, fast dynamic voltage and frequency scaling (DVFS) based power management across the circuit, architecture, and operating system layers is studied through a learning-based hierarchical power management strategy for multi-/many-core systems