Proceedings of the 5th International Workshop on Reconfigurable Communication-centric Systems on Chip 2010 - ReCoSoC\u2710 - May 17-19, 2010 Karlsruhe, Germany. (KIT Scientific Reports ; 7551)

ReCoSoC is intended to be a periodic annual meeting to expose and discuss gathered expertise as well as state of the art research around SoC related topics through plenary invited papers and posters. The workshop aims to provide a prospective view of tomorrow\u27s challenges in the multibillion transistor era, taking into account the emerging techniques and architectures exploring the synergy between flexible on-chip communication and system reconfigurability

Energy-Efficient Interconnection Networks for High-Performance Computing

In recent years, energy has become one of the most important factors for de- signing and operating large scale computing systems. This is particularly true in high-performance computing, where systems often consist of thousands of nodes. Especially after the end of Dennard’s scaling, the demand for energy- proportionality in components, where energy is depending linearly on utilization, increases continuously. As the main contributor to the overall power consumption, processors have received the main attention so far. The increasing energy proportionality of processors, however, shifts the focus to other components such as interconnection networks. Their share of the overall power consumption is expected to increase to 20% or more while other components further increase their efficiency in the near future. Hence, it is crucial to improve energy proportionality in interconnection networks likewise to reduce overall power and energy consumption. To facilitate these attempts, this work provides comprehensive studies about energy saving in interconnection networks at different levels. First, interconnection networks differ fundamentally from other components in their underlying technology. To gain a deeper understanding of these differences and to identify targets for energy savings, this work provides a detailed power analysis of current network hardware. Furthermore, various applications at different scales are analyzed regarding their communication patterns and locality properties. The findings show that communication makes up only a small fraction of the execution time and networks are actually idling most of the time. Another observation is that point-to-point communication often only occurs within various small subsets of all participants, which indicates that a coordinated mapping could further decrease network traffic. Based on these studies, three different energy-saving policies are designed, which all differ in their implementation and focus. Then, these policies are evaluated in an event-based, power-aware network simulator. While two policies that operate completely local at link level, enable significant energy savings of more than 90% in most analyses, the hybrid one does not provide further benefits despite significant additional design effort. Additionally, these studies include network design parameters, such as transition time between different link configurations, as well as the three most common topologies in supercomputing systems. The final part of this work addresses the interactions of congestion management and energy-saving policies. Although both network management strategies aim for different goals and use opposite approaches, they complement each other and can increase energy efficiency in all studies as well as improve the performance overhead as opposed to plain energy saving

NoC-based Architectures for Real-Time Applications : Performance Analysis and Design Space Exploration

Monoprocessor architectures have reached their limits in regard to the computing power they offer vs the needs of modern systems. Although multicore architectures partially mitigate this limitation and are commonly used nowadays, they usually rely on intrinsically non-scalable buses to interconnect the cores. The manycore paradigm was proposed to tackle the scalability issue of bus-based multicore processors. It can scale up to hundreds of processing elements (PEs) on a single chip, by organizing them into computing tiles (holding one or several PEs). Intercore communication is usually done using a Network-on-Chip (NoC) that consists of interconnected onchip routers allowing communication between tiles. However, manycore architectures raise numerous challenges, particularly for real-time applications. First, NoC-based communication tends to generate complex blocking patterns when congestion occurs, which complicates the analysis, since computing accurate worst-case delays becomes difficult. Second, running many applications on large Systems-on-Chip such as manycore architectures makes system design particularly crucial and complex. On one hand, it complicates Design Space Exploration, as it multiplies the implementation alternatives that will guarantee the desired functionalities. On the other hand, once a hardware architecture is chosen, mapping the tasks of all applications on the platform is a hard problem, and finding an optimal solution in a reasonable amount of time is not always possible. Therefore, our first contributions address the need for computing tight worst-case delay bounds in wormhole NoCs. We first propose a buffer-aware worst-case timing analysis (BATA) to derive upper bounds on the worst-case end-to-end delays of constant-bit rate data flows transmitted over a NoC on a manycore architecture. We then extend BATA to cover a wider range of traffic types, including bursty traffic flows, and heterogeneous architectures. The introduced method is called G-BATA for Graph-based BATA. In addition to covering a wider range of assumptions, G-BATA improves the computation time; thus increases the scalability of the method. In a second part, we develop a method addressing design and mapping for applications with real-time constraints on manycore platforms. It combines model-based engineering tools (TTool) and simulation with our analytical verification technique (G-BATA) and tools (WoPANets) to provide an efficient design space exploration framework. Finally, we validate our contributions on (a) a serie of experiments on a physical platform and (b) two case studies taken from the real world: an autonomous vehicle control application, and a 5G signal decoder applicatio

High-Performance and Wavelength-Reused Optical Network on Chip (ONoC) Architectures and Communication Schemes for Manycore Processor

Optical Network on Chip (ONoC) is an emerging chip-scale optical interconnection technology to realize the high-performance and power-efficient inter-core communication for many-core processors. By utilizing the silicon photonic interconnects to transmit data packets with optical signals, it can achieve ultra low communication delay, high bandwidth capacity, and low power dissipation. With the benefits of Wavelength Division Multiplexing (WDM), multiple optical signals can simultaneously be transmitted in the same optical interconnect through different wavelengths. Thus, the WDM-based ONoC is becoming a hot research topic recently. However, the maximal number of available wavelengths is restricted for the reliable and power-efficient optical communication in ONoC. Hence, with a limited number of wavelengths, the design of high-performance and power-efficient ONoC architecture is an important and challenging problem. In this thesis, the design methodology of wavelength-reused ONoC architecture is explored. With the wavelength reuse scheme in optical routing paths, high-performance and power-efficient communication is realized for many-core processors only using a small number of available wavelengths. Three wavelength-reused ONoC architectures and communication schemes are proposed to fulfil different communication requirements, i.e., network scalability, multicast communication, and dark silicon. Firstly, WRH-ONoC, a wavelength-reused hierarchical Optical Network on Chip architecture, is proposed to achieve high network scalability, namely obtaining low communication delay and high throughput capacity for hundreds of thousands of cores by reusing the limited number of available wavelengths with the modest hardware cost and energy overhead. WRH-ONoC combines the advantages of non-blocking communication in each lambda-router and wavelength reuse in all lambda-routers through the hierarchical networking. Both theoretical analysis and simulation results indicate that WRH-ONoC can achieve prominent improvement on the communication performance and scalability (e.g., 46.0% of reduction on the zero-load packet delay and 72.7% of improvement on the network throughput for 400 cores with small hardware cost and energy overhead) in comparison with existing schemes. Secondly, DWRMR, a dynamical wavelength-reused multicast scheme based on the optical multicast ring, is proposed for widely existing multicast communications in many-core processors. In DWRMR, an optical multicast ring is dynamically constructed for each multicast group and the multicast packets are transmitted in a single-send-multi-receive manner requiring only one wavelength. All the cores in the same multicast group can reuse the established multicast ring through an optical token arbitration scheme for the interactive multicast communications, thereby avoiding the frequent construction of multicast routing paths dedicatedly for each core. Simulation results indicate that DWRMR can reduce more than 50% of end-to-end packet delay with slight hardware cost, or require only half number of wavelengths to achieve the same performance compared with existing schemes. Thirdly, Dark-ONoC, a dynamically configurable ONoC architecture, is proposed for the many-core processor with dark silicon. Dark silicon is an inevitable phenomenon that only a small number of cores can be activated simultaneously while the other cores must stay in dark state (power-gated) due to the restricted power budget. Dark-ONoC periodically allocates non-blocking optical routing paths only between the active cores with as less wavelengths as possible. Thus, it can obtain high-performance communication and low power consumption at the same time. Extensive simulations are conducted with the dark silicon patterns from both synthetic distribution and real data traces. The simulation results indicate that the number of wavelengths is reduced by around 15% and the overall power consumption is reduced by 23.4% compared to existing schemes. Finally, this thesis concludes several important principles on the design of wavelength-reused ONoC architecture, and summarizes some perspective issues for the future research

Virtual Runtime Application Partitions for Resource Management in Massively Parallel Architectures

This thesis presents a novel design paradigm, called Virtual Runtime Application Partitions (VRAP), to judiciously utilize the on-chip resources. As the dark silicon era approaches, where the power considerations will allow only a fraction chip to be powered on, judicious resource management will become a key consideration in future designs. Most of the works on resource management treat only the physical components (i.e. computation, communication, and memory blocks) as resources and manipulate the component to application mapping to optimize various parameters (e.g. energy efficiency). To further enhance the optimization potential, in addition to the physical resources we propose to manipulate abstract resources (i.e. voltage/frequency operating point, the fault-tolerance strength, the degree of parallelism, and the configuration architecture). The proposed framework (i.e. VRAP) encapsulates methods, algorithms, and hardware blocks to provide each application with the abstract resources tailored to its needs. To test the efficacy of this concept, we have developed three distinct self adaptive environments: (i) Private Operating Environment (POE), (ii) Private Reliability Environment (PRE), and (iii) Private Configuration Environment (PCE) that collectively ensure that each application meets its deadlines using minimal platform resources. In this work several novel architectural enhancements, algorithms and policies are presented to realize the virtual runtime application partitions efficiently. Considering the future design trends, we have chosen Coarse Grained Reconfigurable Architectures (CGRAs) and Network on Chips (NoCs) to test the feasibility of our approach. Specifically, we have chosen Dynamically Reconfigurable Resource Array (DRRA) and McNoC as the representative CGRA and NoC platforms. The proposed techniques are compared and evaluated using a variety of quantitative experiments. Synthesis and simulation results demonstrate VRAP significantly enhances the energy and power efficiency compared to state of the art.Siirretty Doriast

Understanding and Improving the Latency of DRAM-Based Memory Systems

Over the past two decades, the storage capacity and access bandwidth of main memory have improved tremendously, by 128x and 20x, respectively. These improvements are mainly due to the continuous technology scaling of DRAM (dynamic random-access memory), which has been used as the physical substrate for main memory. In stark contrast with capacity and bandwidth, DRAM latency has remained almost constant, reducing by only 1.3x in the same time frame. Therefore, long DRAM latency continues to be a critical performance bottleneck in modern systems. Increasing core counts, and the emergence of increasingly more data-intensive and latency-critical applications further stress the importance of providing low-latency memory access. In this dissertation, we identify three main problems that contribute significantly to long latency of DRAM accesses. To address these problems, we present a series of new techniques. Our new techniques significantly improve both system performance and energy efficiency. We also examine the critical relationship between supply voltage and latency in modern DRAM chips and develop new mechanisms that exploit this voltage-latency trade-off to improve energy efficiency. The key conclusion of this dissertation is that augmenting DRAM architecture with simple and low-cost features, and developing a better understanding of manufactured DRAM chips together lead to significant memory latency reduction as well as energy efficiency improvement. We hope and believe that the proposed architectural techniques and the detailed experimental data and observations on real commodity DRAM chips presented in this dissertation will enable development of other new mechanisms to improve the performance, energy efficiency, or reliability of future memory systems.Comment: PhD Dissertatio

2.5D Chiplet Architecture for Embedded Processing of High Velocity Streaming Data

This dissertation presents an energy efficient 2.5D chiplet-based architecture for real-time probabilistic processing of high-velocity sensor data, from an autonomous real-time ubiquitous surveillance imaging system. This work addresses problems at all levels of description. At the lowest physical level, new standard cell libraries have been developed for ultra-low voltage CMOS synthesis, as well as custom SRAM memory blocks, and mixed-signal physical true random number generators based on the perturbation of Sigma-Delta structures using random telegraph noise (RTN) in single transistor devices. At the chip level architecture, an innovative compact buffer-less switched circuit mesh network on chip (NoC) capable of reaching very high throughput (1.6Tbps), finite packet delay delivery, free from packet dropping, and free from dead-locks and live-locks, was designed for this chiplet-based solution. Additionally, a second NoC connecting processors in the network, was implemented based on token-rings, allowing access to external DDR memory. Furthermore, a new clock tree distribution network, and a wide bandwidth DRAM physical interface have been designed to address the data flow requirements within and across chiplets. At the algorithm and representation levels, the Online Change Point Detection (CPD) algorithm has been implemented for on-line learning of background-foreground segmentation. Instead of using traditional binary representation of numbers, this architecture relies on unconventional processing of signals using a bio-inspired (spike-based) unary representation of numbers, where these numbers are represented in a stochastic stream of Bernoulli random variables. By using this representation, probabilistic algorithms can be executed in a native architecture with precision on demand, where if more accuracy is required, more computational time and power can be allocated. The SoC chiplet architecture has been extensively simulated and validated using state of the art CAD methodology, and has been submitted to fabrication in a dedicated 55nm GF CMOS technology wafer run. Experimental results from fabricated test chips in the same technology are also presented