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

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

BOOLEAN AND BRAIN-INSPIRED COMPUTING USING SPIN-TRANSFER TORQUE DEVICES

Several completely new approaches (such as spintronic, carbon nanotube, graphene, TFETs, etc.) to information processing and data storage technologies are emerging to address the time frame beyond current Complementary Metal-Oxide-Semiconductor (CMOS) roadmap. The high speed magnetization switching of a nano-magnet due to current induced spin-transfer torque (STT) have been demonstrated in recent experiments. Such STT devices can be explored in compact, low power memory and logic design. In order to truly leverage STT devices based computing, researchers require a re-think of circuit, architecture, and computing model, since the STT devices are unlikely to be drop-in replacements for CMOS. The potential of STT devices based computing will be best realized by considering new computing models that are inherently suited to the characteristics of STT devices, and new applications that are enabled by their unique capabilities, thereby attaining performance that CMOS cannot achieve. The goal of this research is to conduct synergistic exploration in architecture, circuit and device levels for Boolean and brain-inspired computing using nanoscale STT devices. Specifically, we first show that the non-volatile STT devices can be used in designing configurable Boolean logic blocks. We propose a spin-memristor threshold logic (SMTL) gate design, where memristive cross-bar array is used to perform current mode summation of binary inputs and the low power current mode spintronic threshold device carries out the energy efficient threshold operation. Next, for brain-inspired computing, we have exploited different spin-transfer torque device structures that can implement the hard-limiting and soft-limiting artificial neuron transfer functions respectively. We apply such STT based neuron (or ‘spin-neuron’) in various neural network architectures, such as hierarchical temporal memory and feed-forward neural network, for performing “human-like” cognitive computing, which show more than two orders of lower energy consumption compared to state of the art CMOS implementation. Finally, we show the dynamics of injection locked Spin Hall Effect Spin-Torque Oscillator (SHE-STO) cluster can be exploited as a robust multi-dimensional distance metric for associative computing, image/ video analysis, etc. Our simulation results show that the proposed system architecture with injection locked SHE-STOs and the associated CMOS interface circuits can be suitable for robust and energy efficient associative computing and pattern matching

Energy efficient hybrid computing systems using spin devices

Emerging spin-devices like magnetic tunnel junctions (MTJ\u27s), spin-valves and domain wall magnets (DWM) have opened new avenues for spin-based logic design. This work explored potential computing applications which can exploit such devices for higher energy-efficiency and performance. The proposed applications involve hybrid design schemes, where charge-based devices supplement the spin-devices, to gain large benefits at the system level. As an example, lateral spin valves (LSV) involve switching of nanomagnets using spin-polarized current injection through a metallic channel such as Cu. Such spin-torque based devices possess several interesting properties that can be exploited for ultra-low power computation. Analog characteristic of spin current facilitate non-Boolean computation like majority evaluation that can be used to model a neuron. The magneto-metallic neurons can operate at ultra-low terminal voltage of ∼20mV, thereby resulting in small computation power. Moreover, since nano-magnets inherently act as memory elements, these devices can facilitate integration of logic and memory in interesting ways. The spin based neurons can be integrated with CMOS and other emerging devices leading to different classes of neuromorphic/non-Von-Neumann architectures. The spin-based designs involve `mixed-mode\u27 processing and hence can provide very compact and ultra-low energy solutions for complex computation blocks, both digital as well as analog. Such low-power, hybrid designs can be suitable for various data processing applications like cognitive computing, associative memory, and currentmode on-chip global interconnects. Simulation results for these applications based on device-circuit co-simulation framework predict more than ∼100x improvement in computation energy as compared to state of the art CMOS design, for optimal spin-device parameters

Topical Workshop on Electronics for Particle Physics

The purpose of the workshop was to present results and original concepts for electronics research and development relevant to particle physics experiments as well as accelerator and beam instrumentation at future facilities; to review the status of electronics for the LHC experiments; to identify and encourage common efforts for the development of electronics; and to promote information exchange and collaboration in the relevant engineering and physics communities