Energy-Efficient and Reliable Computing in Dark Silicon Era

Dark silicon denotes the phenomenon that, due to thermal and power constraints, the fraction of transistors that can operate at full frequency is decreasing in each technology generation. Moore’s law and Dennard scaling had been backed and coupled appropriately for five decades to bring commensurate exponential performance via single core and later muti-core design. However, recalculating Dennard scaling for recent small technology sizes shows that current ongoing multi-core growth is demanding exponential thermal design power to achieve linear performance increase. This process hits a power wall where raises the amount of dark or dim silicon on future multi/many-core chips more and more. Furthermore, from another perspective, by increasing the number of transistors on the area of a single chip and susceptibility to internal defects alongside aging phenomena, which also is exacerbated by high chip thermal density, monitoring and managing the chip reliability before and after its activation is becoming a necessity. The proposed approaches and experimental investigations in this thesis focus on two main tracks: 1) power awareness and 2) reliability awareness in dark silicon era, where later these two tracks will combine together. In the first track, the main goal is to increase the level of returns in terms of main important features in chip design, such as performance and throughput, while maximum power limit is honored. In fact, we show that by managing the power while having dark silicon, all the traditional benefits that could be achieved by proceeding in Moore’s law can be also achieved in the dark silicon era, however, with a lower amount. Via the track of reliability awareness in dark silicon era, we show that dark silicon can be considered as an opportunity to be exploited for different instances of benefits, namely life-time increase and online testing. We discuss how dark silicon can be exploited to guarantee the system lifetime to be above a certain target value and, furthermore, how dark silicon can be exploited to apply low cost non-intrusive online testing on the cores. After the demonstration of power and reliability awareness while having dark silicon, two approaches will be discussed as the case study where the power and reliability awareness are combined together. The first approach demonstrates how chip reliability can be used as a supplementary metric for power-reliability management. While the second approach provides a trade-off between workload performance and system reliability by simultaneously honoring the given power budget and target reliability

Design Space Exploration and Resource Management of Multi/Many-Core Systems

The increasing demand of processing a higher number of applications and related data on computing platforms has resulted in reliance on multi-/many-core chips as they facilitate parallel processing. However, there is a desire for these platforms to be energy-efficient and reliable, and they need to perform secure computations for the interest of the whole community. This book provides perspectives on the aforementioned aspects from leading researchers in terms of state-of-the-art contributions and upcoming trends

Self-adaptivity of applications on network on chip multiprocessors: the case of fault-tolerant Kahn process networks

Technology scaling accompanied with higher operating frequencies and the ability to integrate more functionality in the same chip has been the driving force behind delivering higher performance computing systems at lower costs. Embedded computing systems, which have been riding the same wave of success, have evolved into complex architectures encompassing a high number of cores interconnected by an on-chip network (usually identified as Multiprocessor System-on-Chip). However these trends are hindered by issues that arise as technology scaling continues towards deep submicron scales. Firstly, growing complexity of these systems and the variability introduced by process technologies make it ever harder to perform a thorough optimization of the system at design time. Secondly, designers are faced with a reliability wall that emerges as age-related degradation reduces the lifetime of transistors, and as the probability of defects escaping post-manufacturing testing is increased. In this thesis, we take on these challenges within the context of streaming applications running in network-on-chip based parallel (not necessarily homogeneous) systems-on-chip that adopt the no-remote memory access model. In particular, this thesis tackles two main problems: (1) fault-aware online task remapping, (2) application-level self-adaptation for quality management. For the former, by viewing fault tolerance as a self-adaptation aspect, we adopt a cross-layer approach that aims at graceful performance degradation by addressing permanent faults in processing elements mostly at system-level, in particular by exploiting redundancy available in multi-core platforms. We propose an optimal solution based on an integer linear programming formulation (suitable for design time adoption) as well as heuristic-based solutions to be used at run-time. We assess the impact of our approach on the lifetime reliability. We propose two recovery schemes based on a checkpoint-and-rollback and a rollforward technique. For the latter, we propose two variants of a monitor-controller- adapter loop that adapts application-level parameters to meet performance goals. We demonstrate not only that fault tolerance and self-adaptivity can be achieved in embedded platforms, but also that it can be done without incurring large overheads. In addressing these problems, we present techniques which have been realized (depending on their characteristics) in the form of a design tool, a run-time library or a hardware core to be added to the basic architecture

Driving the Network-on-Chip Revolution to Remove the Interconnect Bottleneck in Nanoscale Multi-Processor Systems-on-Chip

The sustained demand for faster, more powerful chips has been met by the availability of chip manufacturing processes allowing for the integration of increasing numbers of computation units onto a single die. The resulting outcome, especially in the embedded domain, has often been called SYSTEM-ON-CHIP (SoC) or MULTI-PROCESSOR SYSTEM-ON-CHIP (MP-SoC). MPSoC design brings to the foreground a large number of challenges, one of the most prominent of which is the design of the chip interconnection. With a number of on-chip blocks presently ranging in the tens, and quickly approaching the hundreds, the novel issue of how to best provide on-chip communication resources is clearly felt. NETWORKS-ON-CHIPS (NoCs) are the most comprehensive and scalable answer to this design concern. By bringing large-scale networking concepts to the on-chip domain, they guarantee a structured answer to present and future communication requirements. The point-to-point connection and packet switching paradigms they involve are also of great help in minimizing wiring overhead and physical routing issues. However, as with any technology of recent inception, NoC design is still an evolving discipline. Several main areas of interest require deep investigation for NoCs to become viable solutions: • The design of the NoC architecture needs to strike the best tradeoff among performance, features and the tight area and power constraints of the onchip domain. • Simulation and verification infrastructure must be put in place to explore, validate and optimize the NoC performance. • NoCs offer a huge design space, thanks to their extreme customizability in terms of topology and architectural parameters. Design tools are needed to prune this space and pick the best solutions. • Even more so given their global, distributed nature, it is essential to evaluate the physical implementation of NoCs to evaluate their suitability for next-generation designs and their area and power costs. This dissertation performs a design space exploration of network-on-chip architectures, in order to point-out the trade-offs associated with the design of each individual network building blocks and with the design of network topology overall. The design space exploration is preceded by a comparative analysis of state-of-the-art interconnect fabrics with themselves and with early networkon- chip prototypes. The ultimate objective is to point out the key advantages that NoC realizations provide with respect to state-of-the-art communication infrastructures and to point out the challenges that lie ahead in order to make this new interconnect technology come true. Among these latter, technologyrelated challenges are emerging that call for dedicated design techniques at all levels of the design hierarchy. In particular, leakage power dissipation, containment of process variations and of their effects. The achievement of the above objectives was enabled by means of a NoC simulation environment for cycleaccurate modelling and simulation and by means of a back-end facility for the study of NoC physical implementation effects. Overall, all the results provided by this work have been validated on actual silicon layout

Combined on-line lifetime-energy optimization for asymmetric multicores

In this paper we present an architectural and on-line resource management solution to optimize lifetime reliability of asymmetric multicores while minimizing the system energy consumption, targeting both single nodes (multicores) as well as multiple ones (cluster of multicores). The solution exploits the different characteristics of the computing resources to achieve the desired performance while optimizing the lifetime/energy trade-off. The experimental results show that a combined optimization of energy and lifetime allows for achieving an extended lifetime (similar to the one pursued by lifetime-only optimization solutions) with a marginal energy consumption detriment (less than 2%) with respect to energy-aware but aging-unaware systems

Energy consumption in networks on chip : efficiency and scaling

Computer architecture design is in a new era where performance is increased by replicating processing cores on a chip rather than making CPUs larger and faster. This design strategy is motivated by the superior energy efficiency of the multi-core architecture compared to the traditional monolithic CPU. If the trend continues as expected, the number of cores on a chip is predicted to grow exponentially over time as the density of transistors on a die increases. A major challenge to the efficiency of multi-core chips is the energy used for communication among cores over a Network on Chip (NoC). As the number of cores increases, this energy also increases, imposing serious constraints on design and performance of both applications and architectures. Therefore, understanding the impact of different design choices on NoC power and energy consumption is crucial to the success of the multi- and many-core designs. This dissertation proposes methods for modeling and optimizing energy consumption in multi- and many-core chips, with special focus on the energy used for communication on the NoC. We present a number of tools and models to optimize energy consumption and model its scaling behavior as the number of cores increases. We use synthetic traffic patterns and full system simulations to test and validate our methods. Finally, we take a step back and look at the evolution of computer hardware in the last 40 years and, using a scaling theory from biology, present a predictive theory for power-performance scaling in microprocessor systems

Extending the performance of hybrid NoCs beyond the limitations of network heterogeneity

To meet the performance and scalability demands of the fast-paced technological growth towards exascale and Big-Data processing with the performance bottleneck of conventional metal based interconnects (wireline), alternative interconnect fabrics such as inhomogeneous three-dimensional integrated Network-on-Chip (3D NoC) and hybrid wired-wireless Network-on-Chip (WiNoC) have emanated as a cost-effective solution for emerging System-on-Chip (SoC) design. However, these interconnects trade-off optimized performance for cost by restricting the number of area and power hungry 3D routers and wireless nodes. Moreover, the non-uniform distributed traffic in chip multiprocessor (CMP) demands an on-chip communication infrastructure which can avoid congestion under high traffic conditions while possessing minimal pipeline delay at low-load conditions. To this end, in this paper, we propose a low-latency adaptive router with a low-complexity single-cycle bypassing mechanism to alleviate the performance degradation due to the slow 2D routers in such emerging hybrid NoCs. The proposed router transmits a flit using dimension-ordered routing (DoR) in the bypass datapath at low-loads. When the output port required for intra-dimension bypassing is not available, the packet is routed adaptively to avoid congestion. The router also has a simplified virtual channel allocation (VA) scheme that yields a non-speculative low-latency pipeline. By combining the low-complexity bypassing technique with adaptive routing, the proposed router is able balance the traffic in hybrid NoCs to achieve low-latency communication under various traffic loads. Simulation shows that, the proposed router can reduce applications’ execution time by an average of 16.9% compared to low-latency routers such as SWIFT. By reducing the latency between 2D routers (or wired nodes) and 3D routers (or wireless nodes) the proposed router can improve performance efficiency in terms of average packet delay by an average of 45% (or 50%) in 3D NoCs (or WiNoCs)

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