Reducing Power Loss, Cost and Complexity of SoC Power Delivery Using Integrated 3-Level Voltage Regulators

Traditional methods of system-on-chip (SoC) power management based on dynamic voltage and frequency scaling (DVFS) is limited by 1) cores/IP blocks sharing a voltage domain provided by off-chip voltage regulators (VR) and 2) slow voltage scaling time $(<0.1V/\mu s)$. This global, slow DVFS cannot track the increasingly heterogeneous, fluctuating performance requirements of individual microprocessor cores and SoC components. Furthermore, traditional off-chip VRs add significant area overhead and component cost on the board. This thesis explores replacing a large portion of existing off-chip VRs with integrated voltage regulators (IVR) that can scale the voltage at a 50 mV/ns rate, which is 500 times faster than microsecond-scale voltage scaling with existing off-chip VRs. IVRs occupy 10 times smaller footprint than off-chip VRs, making it easy to duplicate them to provide per-core or per-IP-block voltage control. This thesis starts by summarizing the benefits of using IVRs to deliver power to SoCs. Based on a simulation study targeting a 1.6W, 4-core SoC, I show that greater than 20% energy savings is possible with fast, per-core DVFS enabled by IVRs. Next, I present two stand-alone IVR test-chips converting 1.8V and 2.4V to 0.4-1.4V while delivering maximum 1W to the output. Both test-chips incorporate a 3-level VR topology, which is suitable for integration because the topology allows for much smaller inductors (1nH) than existing inductor-based buck VRs. I also discuss reasons behind lower-than-simulated efficiencies in the test-chips and ways to improve. Finally, I conclude with future process technologies that can boost IVR conversion efficiencies and power densities.Engineering and Applied Science

Power delivery mechanisms for asynchronous loads in energy harvesting systems

PhD ThesisFor systems depending on methods, a fundamental contradiction in the power delivery chain has existed between conventional to supply it. DC/DC conversion (e.g.) has therefore been an integral part of such systems to resolve this contradiction. be made tolerant to a much wider range of Vdd variance. This may open up opportunities for much more energy efficient methods of power delivery. performance of different power delivery mechanisms driving both asynchronous and synchronous loads directly from a harvester source bypassing bulky energy method, which employs a energy from a EH circuit depending on load and source conditions, is developed. through comprehensive comparative analysis. Based on the novel CBB power delivery method, an asynchronous controller is circuits to work with tasks. The successful asynchronous control design drives a case study that is meant to explore relations between power path and task path. To deal with different tasks with variable harvested power, systems may have a range of operation conditions and thus dynamically call for CBB or SCC type power set of capacitors to form CBB or SCC is implemented with economic system size. This work presents an unconventional way of designing a compact-size, quick- circuit overcome large voltage variation in EH systems and implement smart power management for harsh EH environment. The power delivery mechanisms (SCC, employed to help asynchronous- logic-based chip testing and micro-scale EH system demonstrations

An Overview of Fully Integrated Switching Power Converters Based on Switched-Capacitor versus Inductive Approach and Their Advanced Control Aspects

This paper reviews and discusses the state of the art of integrated switched-capacitor and integrated inductive power converters and provides a perspective on progress towards the realization of efficient and fully integrated DC–DC power conversion. A comparative assessment has been presented to review the salient features in the utilization of transistor technology between the switched-capacitor and switched inductor converter-based approaches. First, applications that drive the need for integrated switching power converters are introduced, and further implementation issues to be addressed also are discussed. Second, different control and modulation strategies applied to integrated switched-capacitor (voltage conversion ratio control, duty cycle control, switching frequency modulation, Ron modulation, and series low drop out) and inductive converters (pulse width modulation and pulse frequency modulation) are then discussed. Finally, a complete set of integrated power converters are related in terms of their conditions and operation metrics, thereby allowing a categorization to provide the suitability of converter technologies

A Ringamp-Assisted, Output Capacitor-less Analog CMOS Low-Dropout Voltage Regulator

Continued advancements in state-of-the-art integrated circuits have furthered trends toward higher computational performance and increased functionality within smaller circuit area footprints, all while improving power efficiencies to meet the demands of mobile and battery-powered applications. A significant portion of these advancements have been enabled by continued scaling of CMOS technology into smaller process node sizes, facilitating faster digital systems and power optimized computation. However, this scaling has degraded classic analog amplifying circuit structures with reduced voltage headroom and lower device output resistance; and thus, lower available intrinsic gain. This work investigates these trends and their impact for fine-grain Low-Dropout (LDO) Voltage Regulators, leading to a presented design methodology and implementation of a state-of-the-art Ringamp-Assisted, Output Capacitor-less Analog CMOS LDO Voltage Regulator capable of both power scaling and process node scaling for general SoC applications

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

Distributed IC Power Delivery: Stability-Constrained Design Optimization and Workload-Aware Power Management

ABSTRACT Power delivery presents key design challenges in today’s systems ranging from high performance micro-processors to mobile systems-on-a-chips (SoCs). A robust power delivery system is essential to ensure reliable operation of on-die devices. Nowadays it has become an important design trend to place multiple voltage regulators on-chip in a distributive manner to cope with power supply noise. However, stability concern arises because of the complex interactions be-tween multiple voltage regulators and bulky network of the surrounding passive parasitics. The recently developed hybrid stability theorem (HST) is promising to deal with the stability of such system by efficiently capturing the effects of all interactions, however, large overdesign and hence severe performance degradation are caused by the intrinsic conservativeness of the underlying HST framework. To address such challenge, this dissertation first extends the HST by proposing a frequency-dependent system partitioning technique to substantially reduce the pessimism in stability evaluation. By systematically exploring the theoretical foundation of the HST framework, we recognize all the critical constraints under which the partitioning technique can be performed rigorously to remove conservativeness while maintaining key theoretical properties of the partitioned subsystems. Based on that, we develop an efficient stability-ensuring automatic design flow for large power delivery systems with distributed on-chip regulation. In use of the proposed approach, we further discover new design insights for circuit designers such as how regulator topology, on-chip decoupling capacitance, and the number of integrated voltage regulators can be optimized for improved system tradeoffs between stability and performances. Besides stability, power efficiency must be improved in every possible way while maintaining high power quality. It can be argued that the ultimate power integrity and efficiency may be best achieved via a heterogeneous chain of voltage processing starting from on-board switching voltage regulators (VRs), to on-chip switching VRs, and finally to networks of distributed on-chip linear VRs. As such, we propose a heterogeneous voltage regulation (HVR) architecture encompassing regulators with complimentary characteristics in response time, size, and efficiency. By exploring the rich heterogeneity and tunability in HVR, we develop systematic workload-aware control policies to adapt heterogeneous VRs with respect to workload change at multiple temporal scales to significantly improve system power efficiency while providing a guarantee for power integrity. The proposed techniques are further supported by hardware-accelerated machine learning prediction of non-uniform spatial workload distributions for more accurate HVR adaptation at fine time granularity. Our evaluations based on the PARSEC benchmark suite show that the proposed adaptive 3-stage HVR reduces the total system energy dissipation by up to 23.9% and 15.7% on average compared with the conventional static two-stage voltage regulation using off- and on-chip switching VRs. Compared with the 3-stage static HVR, our runtime control reduces system energy by up to 17.9% and 12.2% on average. Furthermore, the proposed machine learning prediction offers up to 4.1% reduction of system energy

Integrated Circuits and Systems for Smart Sensory Applications

Connected intelligent sensing reshapes our society by empowering people with increasing new ways of mutual interactions. As integration technologies keep their scaling roadmap, the horizon of sensory applications is rapidly widening, thanks to myriad light-weight low-power or, in same cases even self-powered, smart devices with high-connectivity capabilities. CMOS integrated circuits technology is the best candidate to supply the required smartness and to pioneer these emerging sensory systems. As a result, new challenges are arising around the design of these integrated circuits and systems for sensory applications in terms of low-power edge computing, power management strategies, low-range wireless communications, integration with sensing devices. In this Special Issue recent advances in application-specific integrated circuits (ASIC) and systems for smart sensory applications in the following five emerging topics: (I) dedicated short-range communications transceivers; (II) digital smart sensors, (III) implantable neural interfaces, (IV) Power Management Strategies in wireless sensor nodes and (V) neuromorphic hardware

2022 roadmap on neuromorphic computing and engineering

Modern computation based on von Neumann architecture is now a mature cutting-edge science. In the von Neumann architecture, processing and memory units are implemented as separate blocks interchanging data intensively and continuously. This data transfer is responsible for a large part of the power consumption. The next generation computer technology is expected to solve problems at the exascale with 10$^{18}$ calculations each second. Even though these future computers will be incredibly powerful, if they are based on von Neumann type architectures, they will consume between 20 and 30 megawatts of power and will not have intrinsic physically built-in capabilities to learn or deal with complex data as our brain does. These needs can be addressed by neuromorphic computing systems which are inspired by the biological concepts of the human brain. This new generation of computers has the potential to be used for the storage and processing of large amounts of digital information with much lower power consumption than conventional processors. Among their potential future applications, an important niche is moving the control from data centers to edge devices. The aim of this roadmap is to present a snapshot of the present state of neuromorphic technology and provide an opinion on the challenges and opportunities that the future holds in the major areas of neuromorphic technology, namely materials, devices, neuromorphic circuits, neuromorphic algorithms, applications, and ethics. The roadmap is a collection of perspectives where leading researchers in the neuromorphic community provide their own view about the current state and the future challenges for each research area. We hope that this roadmap will be a useful resource by providing a concise yet comprehensive introduction to readers outside this field, for those who are just entering the field, as well as providing future perspectives for those who are well established in the neuromorphic computing community