Low Power Processor Architectures and Contemporary Techniques for Power Optimization – A Review

The technological evolution has increased the number of transistors for a given die area significantly and increased the switching speed from few MHz to GHz range. Such inversely proportional decline in size and boost in performance consequently demands shrinking of supply voltage and effective power dissipation in chips with millions of transistors. This has triggered substantial amount of research in power reduction techniques into almost every aspect of the chip and particularly the processor cores contained in the chip. This paper presents an overview of techniques for achieving the power efficiency mainly at the processor core level but also visits related domains such as buses and memories. There are various processor parameters and features such as supply voltage, clock frequency, cache and pipelining which can be optimized to reduce the power consumption of the processor. This paper discusses various ways in which these parameters can be optimized. Also, emerging power efficient processor architectures are overviewed and research activities are discussed which should help reader identify how these factors in a processor contribute to power consumption. Some of these concepts have been already established whereas others are still active research areas. © 2009 ACADEMY PUBLISHER

Floorplan-Aware High Performance NoC Design

Las actuales arquitecturas de m�ltiples n�cleos como los chip multiprocesadores (CMP) y soluciones multiprocesador para sistemas dentro del chip (MPSoCs) han adoptado a las redes dentro del chip (NoC) como elemento -ptimo para la inter-conexi-n de los diversos elementos de dichos sistemas. En este sentido, fabricantes de CMPs y MPSoCs han adoptado NoCs sencillas, generalmente con una topolog'a en malla o anillo, ya que son suficientes para satisfacer las necesidades de los sistemas actuales. Sin embargo a medida que los requerimientos del sistema -- baja latencia y alto rendimiento -- se hacen m�s exigentes, estas redes tan simples dejan de ser una soluci-n real. As', la comunidad investigadora ha propuesto y analizado NoCs m�s complejas. No obstante, estas soluciones son m�s dif'ciles de implementar -- especialmente los enlaces largos -- haciendo que este tipo de topolog'as complejas sean demasiado costosas o incluso inviables. En esta tesis, presentamos una metodolog'a de dise-o que minimiza la p�rdida de prestaciones de la red debido a su implementaci-n real. Los principales problemas que se encuentran al implementar una NoC son los conmutadores y los enlaces largos. En esta tesis, el conmutador se ha hecho modular, es decir, formado como uni-n de m-dulos m�s peque-os. En nuestro caso, los m-dulos son id�nticos, donde cada m-dulo es capaz de arbitrar, conmutar, y almacenar los mensajes que le llegan. Posteriormente, flexibilizamos la colocaci-n de estos m-dulos en el chip, permitiendo que m-dulos de un mismo conmutador est�n distribuidos por el chip. Esta metodolog'a de dise-o la hemos aplicado a diferentes escenarios. Primeramente, hemos introducido nuestro conmutador modular en NoCs con topolog'as conocidas como la malla 2D. Los resultados muestran como la modularidad y la distribuci-n del conmutador reducen la latencia y el consumo de potencia de la red. En segundo lugar, hemos utilizado nuestra metodolog'a de dise-o para implementar un crossbar distribuidRoca Pérez, A. (2012). Floorplan-Aware High Performance NoC Design [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/17844Palanci

Configurable data center switch architectures

In this thesis, we explore alternative architectures for implementing con_gurable Data Center Switches along with the advantages that can be provided by such switches. Our first contribution centers around determining switch architectures that can be implemented on Field Programmable Gate Array (FPGA) to provide configurable switching protocols. In the process, we identify a gap in the availability of frameworks to realistically evaluate the performance of switch architectures in data centers and contribute a simulation framework that relies on realistic data center traffic patterns. Our framework is then used to evaluate the performance of currently existing as well as newly proposed FPGA-amenable switch designs. Through collaborative work with Meng and Papaphilippou, we establish that only small-medium range switches can be implemented on today's FPGAs. Our second contribution is a novel switch architecture that integrates a custom in-network hardware accelerator with a generic switch to accelerate Deep Neural Network training applications in data centers. Our proposed accelerator architecture is prototyped on an FPGA, and a scalability study is conducted to demonstrate the trade-offs of an FPGA implementation when compared to an ASIC implementation. In addition to the hardware prototype, we contribute a light weight load-balancing and congestion control protocol that leverages the unique communication patterns of ML data-parallel jobs to enable fair sharing of network resources across different jobs. Our large-scale simulations demonstrate the ability of our novel switch architecture and light weight congestion control protocol to both accelerate the training time of machine learning jobs by up to 1.34x and benefit other latency-sensitive applications by reducing their 99%-tile completion time by up to 4.5x. As for our final contribution, we identify the main requirements of in-network applications and propose a Network-on-Chip (NoC)-based architecture for supporting a heterogeneous set of applications. Observing the lack of tools to support such research, we provide a tool that can be used to evaluate NoC-based switch architectures.Open Acces

Quarc: an architecture for efficient on-chip communication

The exponential downscaling of the feature size has enforced a paradigm shift from computation-based design to communication-based design in system on chip development. Buses, the traditional communication architecture in systems on chip, are incapable of addressing the increasing bandwidth requirements of future large systems. Networks on chip have emerged as an interconnection architecture offering unique solutions to the technological and design issues related to communication in future systems on chip. The transition from buses as a shared medium to networks on chip as a segmented medium has given rise to new challenges in system on chip realm. By leveraging the shared nature of the communication medium, buses have been highly efficient in delivering multicast communication. The segmented nature of networks, however, inhibits the multicast messages to be delivered as efficiently by networks on chip. Relying on extensive research on multicast communication in parallel computers, several network on chip architectures have offered mechanisms to perform the operation, while conforming to resource constraints of the network on chip paradigm. Multicast communication in majority of these networks on chip is implemented by establishing a connection between source and all multicast destinations before the message transmission commences. Establishing the connections incurs an overhead and, therefore, is not desirable; in particular in latency sensitive services such as cache coherence. To address high performance multicast communication, this research presents Quarc, a novel network on chip architecture. The Quarc architecture targets an area-efficient, low power, high performance implementation. The thesis covers a detailed representation of the building blocks of the architecture, including topology, router and network interface. The cost and performance comparison of the Quarc architecture against other network on chip architectures reveals that the Quarc architecture is a highly efficient architecture. Moreover, the thesis introduces novel performance models of complex traffic patterns, including multicast and quality of service-aware communication

Neural networks-on-chip for hybrid bio-electronic systems

PhD ThesisBy modelling the brains computation we can further our understanding of its function and develop novel treatments for neurological disorders. The brain is incredibly powerful and energy e cient, but its computation does not t well with the traditional computer architecture developed over the previous 70 years. Therefore, there is growing research focus in developing alternative computing technologies to enhance our neural modelling capability, with the expectation that the technology in itself will also bene t from increased awareness of neural computational paradigms. This thesis focuses upon developing a methodology to study the design of neural computing systems, with an emphasis on studying systems suitable for biomedical experiments. The methodology allows for the design to be optimized according to the application. For example, di erent case studies highlight how to reduce energy consumption, reduce silicon area, or to increase network throughput. High performance processing cores are presented for both Hodgkin-Huxley and Izhikevich neurons incorporating novel design features. Further, a complete energy/area model for a neural-network-on-chip is derived, which is used in two exemplar case-studies: a cortical neural circuit to benchmark typical system performance, illustrating how a 65,000 neuron network could be processed in real-time within a 100mW power budget; and a scalable highperformance processing platform for a cerebellar neural prosthesis. From these case-studies, the contribution of network granularity towards optimal neural-network-on-chip performance is explored

A Scalable and Adaptive Network on Chip for Many-Core Architectures

In this work, a scalable network on chip (NoC) for future many-core architectures is proposed and investigated. It supports different QoS mechanisms to ensure predictable communication. Self-optimization is introduced to adapt the energy footprint and the performance of the network to the communication requirements. A fault tolerance concept allows to deal with permanent errors. Moreover, a template-based automated evaluation and design methodology and a synthesis flow for NoCs is introduced

Physical parameter-aware Networks-on-Chip design

PhD ThesisNetworks-on-Chip (NoCs) have been proposed as a scalable, reliable and power-efficient communication fabric for chip multiprocessors (CMPs) and multiprocessor systems-on-chip (MPSoCs). NoCs determine both the performance and the reliability of such systems, with a significant power demand that is expected to increase due to developments in both technology and architecture. In terms of architecture, an important trend in many-core systems architecture is to increase the number of cores on a chip while reducing their individual complexity. This trend increases communication power relative to computation power. Moreover, technology-wise, power-hungry wires are dominating logic as power consumers as technology scales down. For these reasons, the design of future very large scale integration (VLSI) systems is moving from being computation-centric to communication-centric. On the other hand, chip’s physical parameters integrity, especially power and thermal integrity, is crucial for reliable VLSI systems. However, guaranteeing this integrity is becoming increasingly difficult with the higher scale of integration due to increased power density and operating frequencies that result in continuously increasing temperature and voltage drops in the chip. This is a challenge that may prevent further shrinking of devices. Thus, tackling the challenge of power and thermal integrity of future many-core systems at only one level of abstraction, the chip and package design for example, is no longer sufficient to ensure the integrity of physical parameters. New designtime and run-time strategies may need to work together at different levels of abstraction, such as package, application, network, to provide the required physical parameter integrity for these large systems. This necessitates strategies that work at the level of the on-chip network with its rising power budget. This thesis proposes models, techniques and architectures to improve power and thermal integrity of Network-on-Chip (NoC)-based many-core systems. The thesis is composed of two major parts: i) minimization and modelling of power supply variations to improve power integrity; and ii) dynamic thermal adaptation to improve thermal integrity. This thesis makes four major contributions. The first is a computational model of on-chip power supply variations in NoCs. The proposed model embeds a power delivery model, an NoC activity simulator and a power model. The model is verified with SPICE simulation and employed to analyse power supply variations in synthetic and real NoC workloads. Novel observations regarding power supply noise correlation with different traffic patterns and routing algorithms are found. The second is a new application mapping strategy aiming vii to minimize power supply noise in NoCs. This is achieved by defining a new metric, switching activity density, and employing a force-based objective function that results in minimizing switching density. Significant reductions in power supply noise (PSN) are achieved with a low energy penalty. This reduction in PSN also results in a better link timing accuracy. The third contribution is a new dynamic thermal-adaptive routing strategy to effectively diffuse heat from the NoC-based threedimensional (3D) CMPs, using a dynamic programming (DP)-based distributed control architecture. Moreover, a new approach for efficient extension of two-dimensional (2D) partially-adaptive routing algorithms to 3D is presented. This approach improves three-dimensional networkon- chip (3D NoC) routing adaptivity while ensuring deadlock-freeness. Finally, the proposed thermal-adaptive routing is implemented in field-programmable gate array (FPGA), and implementation challenges, for both thermal sensing and the dynamic control architecture are addressed. The proposed routing implementation is evaluated in terms of both functionality and performance. The methodologies and architectures proposed in this thesis open a new direction for improving the power and thermal integrity of future NoC-based 2D and 3D many-core architectures