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

MPSoCBench : um framework para avaliação de ferramentas e metodologias para sistemas multiprocessados em chip

Orientador: Rodolfo Jardim de AzevedoTese (doutorado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: Recentes metodologias e ferramentas de projetos de sistemas multiprocessados em chip (MPSoC) aumentam a produtividade por meio da utilização de plataformas baseadas em simuladores, antes de definir os últimos detalhes da arquitetura. No entanto, a simulação só é eficiente quando utiliza ferramentas de modelagem que suportem a descrição do comportamento do sistema em um elevado nível de abstração. A escassez de plataformas virtuais de MPSoCs que integrem hardware e software escaláveis nos motivou a desenvolver o MPSoCBench, que consiste de um conjunto escalável de MPSoCs incluindo quatro modelos de processadores (PowerPC, MIPS, SPARC e ARM), organizado em plataformas com 1, 2, 4, 8, 16, 32 e 64 núcleos, cross-compiladores, IPs, interconexões, 17 aplicações paralelas e estimativa de consumo de energia para os principais componentes (processadores, roteadores, memória principal e caches). Uma importante demanda em projetos MPSoC é atender às restrições de consumo de energia o mais cedo possível. Considerando que o desempenho do processador está diretamente relacionado ao consumo, há um crescente interesse em explorar o trade-off entre consumo de energia e desempenho, tendo em conta o domínio da aplicação alvo. Técnicas de escalabilidade dinâmica de freqüência e voltagem fundamentam-se em gerenciar o nível de tensão e frequência da CPU, permitindo que o sistema alcance apenas o desempenho suficiente para processar a carga de trabalho, reduzindo, consequentemente, o consumo de energia. Para explorar a eficiência energética e desempenho, foram adicionados recursos ao MPSoCBench, visando explorar escalabilidade dinâmica de voltaegem e frequência (DVFS) e foram validados três mecanismos com base na estimativa dinâmica de energia e taxa de uso de CPUAbstract: Recent design methodologies and tools aim at enhancing the design productivity by providing a software development platform before the definition of the final Multiprocessor System on Chip (MPSoC) architecture details. However, simulation can only be efficiently performed when using a modeling and simulation engine that supports system behavior description at a high abstraction level. The lack of MPSoC virtual platform prototyping integrating both scalable hardware and software in order to create and evaluate new methodologies and tools motivated us to develop the MPSoCBench, a scalable set of MPSoCs including four different ISAs (PowerPC, MIPS, SPARC, and ARM) organized in platforms with 1, 2, 4, 8, 16, 32, and 64 cores, cross-compilers, IPs, interconnections, 17 parallel version of software from well-known benchmarks, and power consumption estimation for main components (processors, routers, memory, and caches). An important demand in MPSoC designs is the addressing of energy consumption constraints as early as possible. Whereas processor performance comes with a high power cost, there is an increasing interest in exploring the trade-off between power and performance, taking into account the target application domain. Dynamic Voltage and Frequency Scaling techniques adaptively scale the voltage and frequency levels of the CPU allowing it to reach just enough performance to process the system workload while meeting throughput constraints, and thereby, reducing the energy consumption. To explore this wide design space for energy efficiency and performance, both for hardware and software components, we provided MPSoCBench features to explore dynamic voltage and frequency scalability (DVFS) and evaluated three mechanisms based on energy estimation and CPU usage rateDoutoradoCiência da ComputaçãoDoutora em Ciência da Computaçã

Opportunistic power reassignment between processor and memory in 3D stacks

The pin count largely determines the cost of a chip package, which is often comparable to the cost of a die. In 3D processor-memory designs, power and ground (P/G) pins can account for the majority of the pins. This is because packages include separate pins for the disjoint processor and memory power delivery networks (PDNs). Supporting separate PDNs and P/G pins for processor and memory is inefficient, as each set has to be provisioned for the worst-case power delivery requirements. In this thesis, we propose to reduce the number of P/G pins of both processor and memory in a 3D design, and dynamically and opportunistically divert some power between the two PDNs on demand. To perform the power transfer, we use a small bidirectional on-chip voltage regulator that connects the two PDNs. Our concept, called Snatch, is effective. It allows the computer to execute code sections with high processor or memory power requirements without having to throttle performance. We evaluate Snatch with simulations of an 8-core multicore stacked with two memory dies. In a set of compute-intensive codes, the processor snatches memory power for 30% of the time on average, speeding-up the codes by up to 23% over advanced turbo-boosting; in memory-intensive codes, the memory snatches processor power. Alternatively, Snatch can reduce the package cost by about 30%

A Construction Kit for Efficient Low Power Neural Network Accelerator Designs

Implementing embedded neural network processing at the edge requires efficient hardware acceleration that couples high computational performance with low power consumption. Driven by the rapid evolution of network architectures and their algorithmic features, accelerator designs are constantly updated and improved. To evaluate and compare hardware design choices, designers can refer to a myriad of accelerator implementations in the literature. Surveys provide an overview of these works but are often limited to system-level and benchmark-specific performance metrics, making it difficult to quantitatively compare the individual effect of each utilized optimization technique. This complicates the evaluation of optimizations for new accelerator designs, slowing-down the research progress. This work provides a survey of neural network accelerator optimization approaches that have been used in recent works and reports their individual effects on edge processing performance. It presents the list of optimizations and their quantitative effects as a construction kit, allowing to assess the design choices for each building block separately. Reported optimizations range from up to 10'000x memory savings to 33x energy reductions, providing chip designers an overview of design choices for implementing efficient low power neural network accelerators

Energy Saving and Virtualization Technologies in Switching

Switching is the key functionality for many devices like electronic Router and Switch, optical Router, Network on Chips (NoCs) and so on. Basically, switching is responsible for moving data unit from one port/location to another (or multiple) port(s)/location(s). In past years, the high capacity, low delay were the main concerns when designing high-end switching unit. As new demands, requests and technologies emerge, flexibility and low power cost switching design become to weight the same as throughput and delay. On one hand, highly flexible (i.e, programming ability) switching can cope with variable needs stem from new applications (i.e, VoIP) and popular user behavior (i.e, p2p downloading); on the other hand, reduce the energy and power dissipation for switching could not only save bills and build echo system but also expand components life time. Many research efforts have been devoted to increase switching flexibility and reduce its power cost. In this thesis work, we consider to exploit virtualization as the main technique to build flexible software router in the first part, then in the second part we draw our attention on energy saving in NoC (i.e, a switching fabric designed to handle the on chip data transmission) and software router. In the first part of the thesis, we consider the virtualization inside Software Routers (SRs). SR, i.e, routers running in commodity Personal Computers (PCs), become an appealing solution compared to traditional Proprietary Routing Devices (PRD) for various reasons such as cost (the multi-vendor hardware used by SRs can be cheap, while the equipment needed by PRDs is more expensive and their training cost is higher), openness (SRs can make use of a large number of open source networking applications, while PRDs are more closed) and flexibility. The forwarding performance provided by SRs has been an obstacle to their deployment in real networks. For this reason, we proposed to aggregate multiple routing units that form an powerful SR known as the Multistage Software Router (MSR) to overcome the performance limitation for a single SR. Our results show that the throughput can increase almost linearly as the number of the internal routing devices. But some other features related to flexibility (such as power saving, programmability, router migration or easy management) have been investigated less than performance previously. We noticed that virtualization techniques become reality thanks to the quick development of the PC architectures, which are now able to easily support several logical PCs running in parallel on the same hardware. Virtualization could provide many flexible features like hardware and software decoupling, encapsulation of virtual machine state, failure recovery and security, to name a few. Virtualization permits to build multiple SRs inside one physical host and a multistage architecture exploiting only logical devices. By doing so, physical resources can be used in a more efficient way, energy savings features (switching on and off device when needed) can be introduced and logical resources could be rented on-demand instead of being owned. Since virtualization techniques are still difficult to deploy, several challenges need to be faced when trying to integrate them into routers. The main aim of the first part in this thesis is to find out the feasibility of the virtualization approach, to build and test virtualized SR (VSR), to implement the MSR exploiting logical, i.e. virtualized, resources, to analyze virtualized routing performance and to propose improvement techniques to VSR and virtual MSR (VMSR). More specifically, we considered different virtualization solutions like VMware, XEN, KVM to build VSR and VMSR, being VMware a closed source solution but with higher performance and XEN/KVM open source solutions. Firstly we built and tested each single component of our multistage architecture (i.e, back-end router, load balancer )inside the virtual infrastructure, then and we extended the performance experiments with more complex scenarios like multiple Back-end Router (BR) or Load Balancer (LB) which cooperate to route packets. Our results show that virtualization could introduce 40~\% performance penalty compare with the hardware only solution. Keep the performance limitation in mind, we developed the whole VMSR and we obtained low throughput with 64B packet flow as expected. To increase the VMSR throughput, two directions could be considered, the first one is to improve the single component ( i.e, VSR) performance and the other is to work from the topology (i.e, best allocation of the VMs into the hardware ) point of view. For the first method, we considered to tune the VSR inside the KVM and we studied closely such as Linux driver, scheduler, interconnect methodology which could impact the performance significantly with proper configuration; then we proposed two ways for the VMs allocation into physical servers to enhance the VMSR performance. Our results show that with good tuning and allocation of VMs, we could minimize the virtualization penalty and get reasonable throughput for running SRs inside virtual infrastructure and add flexibility functionalities into SRs easily. In the second part of the thesis, we consider the energy efficient switching design problem and we focus on two main architecture, the NoC and MSR. As many research works suggest, the energy cost in the Communication Technologies ( ICT ) is constantly increasing. Among the main ICT sectors, a large portion of the energy consumption is contributed by the telecommunication infrastructure and their devices, i.e, router, switch, cell phone, ip TV settle box, storage home gateway etc. More in detail, the linecards, links, System on Chip (SoC) including the transmitter/receiver on these variate devices are the main power consuming units. We firstly present the work on the power reduction of the data transmission in SoC, which is carried out by the NoC. NoC is an approach to design the communication subsystem between different Processing Units (PEs) in a SoC. PEs could be different elements such as CPU, memory, digital signal/analog signal processor etc. Different PEs performs specific tasks depending on the applications running on the chip. Different tasks need to exchange data information among each other, thus flits ( chopped packet with limited header information ) are generated by PEs. The flits are injected into the NoC by the proper interface and routed until reach the destination PEs. For the whole procedure, the NoC behaves as a packet switch network. Studies show that in general the information processing in the PEs only consume 60~\% energy while the remaining 40~\% are consumed by the NoC. More importantly, as the current network designing principle, the NoC capacity is devised to handle the peak load. This is a clear sign for energy saving when the network load is low. In our work, we considered to exploit Dynamic Voltage and Frequency Scaling (DVFS) technique, which can jointly decrease or increase the system voltage and frequency when necessary, i.e, decrease the voltage and frequency at low load scenario to save energy and reduce power dissipation. More precisely, we studied two different NoC architectures for energy saving, namely single plane chip and multi-plane chip architecture. In both cases we have a very strict constraint to be that all the links and transmitter/receivers on the same plane work at the same frequency/voltage to avoid synchronization problem. This is the main difference with many existing works in the literature which usually assume different links can work at different frequency, that is hard to be implemented in reality. For the single plane NoC, we exploited different routing schemas combined with DVFS to reduce the power for the whole chip. Our results haven been compared with the optimal value obtained by modeling the power saving formally as a quadratic programming problem. Results suggest that just by using simple load balancing routing algorithm, we can save considerable energy for the single chip NoC architecture. Furthermore, we noticed that in the single plane NoC architecture, the bottleneck link could limit the DVFS effectiveness. Then we discovered that multiplane NoC architecture is fairly easy to be implemented and it could help with the energy saving. Thus we focus on the multiplane architecture and we found out that DVFS could be more efficient when we concentrate more traffic into one plane and send the remaining flows to other planes. We compared load concentration and load balancing with different power modeling and all simulation results show that load concentration is better compared with load balancing for multiplan NoC architecture. Finally, we also present one of the the energy efficient MSR design technique, which permits the MSR to follow the day-night traffic pattern more efficiently with our on-line energy saving algorithm