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

An algorithm for automatically calculating component current ratings in switched capacitor DC-DC converters

OAPA Switched Capacitor (SC) DC-DC converters, which have traditionally been used for on-chip power supplies, are now being considered for medium to high-power applications. This paper presents a method for automatically synthesising SC converters as well as deriving the expressions for their chargemultipliers. Charge multipliers can be used to calculate important design characteristics such as converter output voltage regulation, efficiency and component current ratings, which can then be used to appraise different topologies. However, whilst a full appraisal of converters also requires component voltage ratings, this work is the first step in developing an automatic software tool that will employ a search-based algorithm to generate optimum SC topologies for a given application. The method is based on the proposition that all SC converters can be synthesised from a so-called "basic cell". The automatic derivation and solution of the charge transfer equations for a traditional Fibonacci SC converter is presented as an example of the proposed method, which is validated against existing analytic equations as well as a detailed Spice simulation. The automatic calculation of the charge-multipliers for two other well-known SC converters is also demonstrated, including a new, arbitrarily generated circuit, which again are validated against detailed Spice simulations

Evaluation and implementation of a 5-level hybrid DC-DC converter

In this work, a hybrid voltage regulator topology is analyzed, implemented, and evaluated. The common topologies of DC-DC converters have proven to be lacking in some aspects, such as integrability for buck converters, or maximum efficiency for switched-capacitor regulators. The hybrid topology tackles these shortcomings by combining the advantages of switched-capacitor and inductor-based voltage regulators. A 5-level buck converter is evaluated, implemented, and compared to other converter implementations using the same components. The 5-Level Buck converter can achieve 5 different levels, allowing it to cover 4 operation regions, each between 2 levels. Accordingly, it covers a wide range of output voltages. By reducing the voltage difference at the inductor input, the 5-level buck converter can use smaller inductor compared to both 3-level and conventional buck converters which makes it cheaper, smaller in size, and much more efficient. Simulations show proper functionality of the 5-Level topology, while putting restrictions on the inductor size, efficiency, and component footprint (or total converter area). A test PCB is implemented for verification of the functionality and experimental measurements show that for the same switching frequency and inductor size, the 5-level buck converter achieves up to 15% efficiency improvement over a conventional buck converter and a 3-level buck converter at certain output voltage ranges. Peak efficiency of 94% has been achieved by the 5-Level hybrid converter, which includes all external switching and conduction losses. The proposed hybrid topology proved to yield high conversion efficiency even in the face of component size limitations, which indicates potential benefit in using multilevel converters for several off-chip as well as on-chip applications

Many-core architectures with time predictable execution Support for hard real-time applications

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2013.Cataloged from PDF version of thesis.Includes bibliographical references (p. 183-193).Hybrid control systems are a growing domain of application. They are pervasive and their complexity is increasing rapidly. Distributed control systems for future "Intelligent Grid" and renewable energy generation systems are demanding high-performance, hard real-time computation, and more programmability. General-purpose computer systems are primarily designed to process data and not to interact with physical processes as required by these systems. Generic general-purpose architectures even with the use of real-time operating systems fail to meet the hard realtime constraints of hybrid system dynamics. ASIC, FPGA, or traditional embedded design approaches to these systems often result in expensive, complicated systems that are hard to program, reuse, or maintain. In this thesis, we propose a domain-specific architecture template targeting hybrid control system applications. Using power electronics control applications, we present new modeling techniques, synthesis methodologies, and a parameterizable computer architecture for these large distributed control systems. We propose a new system modeling approach, called Adaptive Hybrid Automaton, based on previous work in control system theory, that uses a mixed-model abstractions and lends itself well to digital processing. We develop a domain-specific architecture based on this modeling that uses heterogeneous processing units and predictable execution, called MARTHA. We develop a hard real-time aware router architecture to enable deterministic on-chip interconnect network communication. We present several algorithms for scheduling task-based applications onto these types of heterogeneous architectures. We create Heracles, an open-source, functional, parameterized, synthesizable many-core system design toolkit, that can be used to explore future multi/many-core processors with different topologies, routing schemes, processing elements or cores, and memory system organizations. Using the Heracles design tool we build a prototype of the proposed architecture using a state-of-the-art FPGA-based platform, and deploy and test it in actual physical power electronics systems. We develop and release an open-source, small representative set of power electronics system applications that can be used for hard real-time application benchmarking.by Michel A. Kinsy.Ph.D

Circuit and System Level Design Optimization for Power Delivery And Management

As the VLSI technology scales to the nanometer scale, power consumption has become a critical design concern of VLSI circuits. Power gating and dynamic voltage and frequency scaling (DVFS) are two effective power management techniques that are widely utilized in modern chip designs. Various design challenges merge with these power management techniques in nanometer VLSI circuits. For example, power gating introduces unique power integrity issues and trade-offs between switching noise and rush current noise. Assuring power integrity and achieving power efficiency are two highly intertwined design challenges. In addition, these trade-offs significantly vary with the supply voltage. It is difficult to use conventional power-gated power delivery networks (PDNs) to fully meet the involved conflicting design constraints while maximizing power saving and minimizing supply noise. The DVFS controller and the DC-DC power converter are two highly intertwining enablers for DVFS-based systems. However, traditional DVFS techniques treat the design optimizations of the two as separate tasks, giving rise to sub-optimal designs. To address the above research challenges, we propose several circuit and system level design optimization techniques in this dissertation. For power-gated PDN designs, we propose systemic decoupling capacitor (decap) optimization strategies that optimally trade-off between power integrity and leakage saving. First, new global decap and re-routable decap design concepts are proposed to relax the tight interaction between power integrity and leakage power saving of power-gated PDN at a single supply voltage level. Furthermore, we propose to leverage re-routable decaps to provide flexible decap allocation structures to better suit multiple supply voltage levels. The proposed strategies are implemented in an automatic design flow for choosing optimal amount of local decaps, global decaps and re-routable decaps. The proposed techniques significantly increase leakage saving without jeopardizing power integrity. The flexible decap allocations enabled by re-routable decaps lead to optimal design trade-offs for PDNs operating with two supply voltage levels. To improve the effectiveness of DVFS, we analyze the drawbacks of circuit-level only and policy-level only optimizations and the promising opportunities resulted from the cross-layer co-optimization of the DC-DC converter and online learning based DVFS polices. We present a cross-layer approach that optimizes transition time, area, energy overhead of the DC-DC converter along with key parameters of an online learning DVFS controller. We systematically evaluate the benefits of the proposed co-optimization strategy based on several processor architectures, namely single and dual-core processors and processors with DVFS and power gating. Our results indicate that the co-optimization can introduce noticeable additional energy saving without significant performance degradation

An FPGA-based infrastructure for fine-grained DVFS analysis in high-performance embedded systems

Emerging technologies provide SoCs with fine-grained DVFS capabilities both in space (number of domains) and time (transients in the order of tens of nanoseconds). Analyzing these systems requires cycle-accurate accounting of rapidly-changing dynamics and complex interactions among accelerators, interconnect, memory, and OS. We present an FPGA-based infrastructure that facilitates such analyses for high-performance embedded systems. We show how our infrastructure can be used to first generate SoCs with loosely-coupled accelerators, and then perform design-space exploration considering several DVFS policies under full-system workload scenarios, sweeping spatial and temporal domain granularity

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

Techniques of Energy-Efficient VLSI Chip Design for High-Performance Computing

How to implement quality computing with the limited power budget is the key factor to move very large scale integration (VLSI) chip design forward. This work introduces various techniques of low power VLSI design used for state of art computing. From the viewpoint of power supply, conventional in-chip voltage regulators based on analog blocks bring the large overhead of both power and area to computational chips. Motivated by this, a digital based switchable pin method to dynamically regulate power at low circuit cost has been proposed to make computing to be executed with a stable voltage supply. For one of the widely used and time consuming arithmetic units, multiplier, its operation in logarithmic domain shows an advantageous performance compared to that in binary domain considering computation latency, power and area. However, the introduced conversion error reduces the reliability of the following computation (e.g. multiplication and division.). In this work, a fast calibration method suppressing the conversion error and its VLSI implementation are proposed. The proposed logarithmic converter can be supplied by dc power to achieve fast conversion and clocked power to reduce the power dissipated during conversion. Going out of traditional computation methods and widely used static logic, neuron-like cell is also studied in this work. Using multiple input floating gate (MIFG) metal-oxide semiconductor field-effect transistor (MOSFET) based logic, a 32-bit, 16-operation arithmetic logic unit (ALU) with zipped decoding and a feedback loop is designed. The proposed ALU can reduce the switching power and has a strong driven-in capability due to coupling capacitors compared to static logic based ALU. Besides, recent neural computations bring serious challenges to digital VLSI implementation due to overload matrix multiplications and non-linear functions. An analog VLSI design which is compatible to external digital environment is proposed for the network of long short-term memory (LSTM). The entire analog based network computes much faster and has higher energy efficiency than the digital one

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

Modeling, Design and Optimization of IC Power Delivery with On-Chip Regulation

As IC technology continues to follow the Moore’s Law, IC designers have been constantly challenged with power delivery issues. While useful power must be reliably delivered to the on-die functional circuits to fulfill the desired functionality and performance, additional power overheads arise due to the loss associated with voltage conversion and parasitic resistance in the metal wires. Hence, one of the key IC power delivery design challenges is to develop voltage conversion/regulation circuits and the corresponding design strategies to provide a guaranteed level of power integrity while achieving high power efficiency and low area overhead. On-chip voltage regulation, a significant ongoing design trend, offers appealing active supply noise suppression close to the loads and is well positioned to address many power delivery challenges. However, to realize the full potential of on-chip voltage regulation requires systemic optimization of and tradeoffs among settling time, steady-state error, power supply noise, power efficiency, stability and area overhead, which are the key focuses of this dissertation. First, we develop new low-dropout voltage regulators (LDOs) that are well optimized for low power applications. To this end, dropout voltage, bias current and speed are important competing design objectives. This dissertation presents new flipped voltage follower (FVF) based topologies of on-chip voltage regulators that handle ultra-fast load transients in nanoseconds while achieving significant improvement on bias current consumption. An active frequency compensation is embedded to achieve high area efficiency by employing a smaller amount of compensation capacitors, the major silicon area contributor. Furthermore, in one of the proposed topologies an auxiliary digital feedback loop is employed in order to lower quiescent power consumption further. Second, coping with supply noise is becoming increasingly more difficult as design complexity grows, which leads to increased spatial and temporal load heterogeneity, and hence larger voltage variations in a given power domain. Addressing this challenge through a distributed methodology wherein multiple voltage regulators are placed across the same voltage domain is particularly promising. This distributive nature allows for even faster suppression of multiple hot spots by the nearby regulators within the power domain and can significantly boost power integrity. Nevertheless, reasoning about the stability of such distributively regulated power networks becomes rather complicated as a result of complex interactions between multiple active regulators and the large passive subnetwork. Coping with this stability challenge requires new theory and stability-ensuring design practice, as targeted by this dissertation. For the first time, we adopt and develop a hybrid stability framework for large power delivery networks with distributed voltage regulation. This framework is local in the sense that both the checking and assurance of network stability can be dealt with on the basis of each individual voltage regulator, leading to feasible design of large power delivery networks that would be computationally impossible otherwise. Accordingly, we propose a new hybrid stability margin concept, examine its tradeoffs with power efficiency, supply noise and silicon area, and demonstrate the resulted key design implications pertaining to new stability-ensuring LDO circuit design techniques and circuit topologies. Finally, we develop an automated hybrid stability design flow that is computationally efficient and provides a practical guarantee of network stability