Industry is moving towards large-scale hardware systems that bundle processor cores, memories, accelerators, and so on. via 2.5D integration. These components are fabricated separately as chiplets and then integrated using an interposer as an interconnect carrier. This new design style is beneficial in terms of yield and economies of scale, as chiplets may come from various vendors and are relatively easy to integrate into one larger sophisticated system. However, the benefits of this approach come at the cost of new security challenges, especially when integrating chiplets that come from untrusted or not fully trusted, third- party vendors. In this work, we explore these challenges for modern interposer-based systems of cache-coherent, multi-core chiplets. First, we present basic coherence-oriented hardware Trojan attacks that pose a significant threat to chiplet-based designs and demonstrate how these basic attacks can be orchestrated to pose a significant threat to interposer-based systems. Second, we propose a novel scheme using an active interposer as a generic, secure-by-construction platform that forms a physical root of trust for modern 2.5D systems. The implementation of our scheme is confined to the interposer, resulting in little cost and leaving the chiplets and coherence system untouched. We show that our scheme prevents a range of coherence attacks with low overheads on system performance, ∼4%. Further, we demonstrate that our scheme scales efficiently as system size and memory capacities increase, resulting in reduced performance overheads.

Programmable metasurfaces constitute an emerging paradigm, envisaged to become a key enabling technology for Reconfigurable Intelligent Surfaces (RIS) due to their powerful control over electromagnetic waves. The HyperSurface (HSF) paradigm takes one step further by embedding a network of customized integrated circuit (IC) controllers within the device with the aim of adding intelligence, connectivity, and autonomy. However, little is known about the traffic that the network needs to support as the target electromagnetic function or boundary conditions change. In this paper, the framework of a methodology is introduced to characterize the workload of programmable metasurfaces which is then used to analyze the beam steering HSFs. The workload characterization leads to many useful insights into traffic behavior, including the spatio-temporal load incurred and the HSF limitations in terms of fine-grained tracking of moving targets. It is observed that the traffic is inherently bursty with an uneven spatial distribution of load and that finer resolution comes at the cost of an increased but less bursty load. An indoor mobility model indicates reasonable signaling load on the deployed surfaces. Finally, a statistical analysis on the traffic patterns is performed, showing that the incoming traffic can be well represented by an ON-OFF model.

FinFET, a non-planar device, has become the prevalent choice for chip-multiprocessor (CMP) designs due to its lower leakage and improved scalability as compared to planar CMOS devices. FinFETs are fundamentally different from conventional CMOS circuits in terms of circuit-delay vs. temperature, i.e., circuit-delay decreases in FinFET at higher temperature even in the super threshold supply-voltage regime. Such characteristic of FinFET is known as temperature effect inversion (TEI). But, a drastic increase in channel temperature may lead to an increase in leakage consumption and may accelerate the circuit aging process due to the self-heating effect (SHE). This paper introduces STIFF, which balances the upsides of TEI against the potential hazardous SHE in a FinFET based CMP. Basically, STIFF exploits online performance statistics to determine the thermal intensity of cores and local caches, and scales the supply-voltage prudentially to maintain a stable core-frequency and local-cache performance on-the-fly by exploiting TEI, while reducing the SHE. Our simulation results show that, STIFF is able to maintain a stable frequency of 3.7GHz of the cores with a small standard deviation of 0.23, while maintaining a safe temperature during execution, and it outperforms a state-of-the-art DVFS technique for the FinFET based cores. STIFF also maintains a stable access time at the local L1 caches, while ensuring thermal safety by introducing a cache access cognizant scaling of the supply voltage of the individual L1 cache-banks without any noticeable performance-loss.

For the first time, we leverage the 2.5D interposer technology to establish system-level security in the face of hardware- and software-centric adversaries. More specifically, we integrate chiplets (i.e., third-party hard intellectual property of complex functionality, like microprocessors) using a security-enforcing interposer. Such hardware organization provides a robust 2.5D root of trust for trustworthy, yet powerful and flexible, computation systems. The security paradigms for our scheme, employed firmly by design and construction, are: 1) stringent physical separation of trusted from untrusted components and 2) runtime monitoring. The system-level activities of all untrusted commodity chiplets are checked continuously against security policiesvia physically separated security features. Aside from the security promises, the good economics of outsourced supply chains are still maintained; the system vendor is free to procure chiplets from the open market, while only producing the interposer and assembling the 2.5D system oneself. We showcase our scheme using the Cortex-M0 core and the AHB-Lite bus by ARM, building a secure 64-core system with shared memories. We evaluate our scheme through hardware simulation, considering different threat scenarios. Finally, we devise a physical-design flow for 2.5D systems, based on commercial-grade design tools, to demonstrate and evaluate our 2.5D root of trust.

Negative Bias Temperature Instability (NBTI) is a prominent physical failure mechanism which severely degrades the performance of PMOS transistors whenever the voltage at the gate is negatively biased. It leads to catastrophic timing violations in critical circuits and a severe shortening of the overall operational lifetime of the entire system. To alleviate such damaging effects due to NBTI, we present PRITEXT, a novel technique which generates a minimal set of deterministic exercise vectors based on test generation techniques which inherently near-optimizes the bit patterns across each of the generated vectors; the end target being to exercise the critical paths of a device when dormant so as to achieve near-ideal NBTI stress reduction. We explore the design-space of our generated vectors and apply them to our test processor platform under differing sequences, where our evaluation under realistic benchmarks shows that PRITEXT leads to an average 4.99× and a maximum of 13.91× lifetime improvement using 9 generated vectors. In an attempt to reduce hardware overheads even further, we next propose a heuristic to further reduce the number of exercise vectors with minimum loss in lifetime improvement.

On-chip nanophotonics offer high throughput, yet energy-efficient communication, traits that can prove critical to the continuance of multicore chip scalability. In this paper, we investigate and propose silicon nanophotonic components that are embedded entirely in the silica (SiO2) substrate, i.e., reside subsurface, as opposed to die on-surface silicon nanophotonics of prior-art. Among several offered advantages, such silicon-in-silica (SiS) nanophotonic structures empower the implementation of nonobstructive interconnect geometries that deliver an improved power-performance balance, as demonstrated experimentally. First, using exhaustive simulations based on commercial-grade optical software-based tools, we show that such SiS structures are feasible, and derive their geometry characteristics and design parameters. As a second step, utilizing SiS optical channels and filters, we then design two distinct SiS-based nanophotonic network-on-chip (PNoC) mesh-diagonal links topologies as a means of demonstrating our proof of concept. In further pushing the performance envelope, we next develop: 1) an associated contention-aware adaptive routing function and 2) a parallelized photonic channel allocation scheme, with both coupled to SiS-based PNoCs as elements, to respectively replace under-performing routing and flow-control photonic protocols currently utilized. An extensive experimental evaluation, including utilizing traffic benchmarks gathered from full-system chip multiprocessor simulations, shows that our methodology boosts network throughput by up to 59.7%, reduces communication latency by up to 78.7%, while improving the throughput-to-power ratio by up to 31.6% when compared to the state-of-the-art.

Traffic hotspots, a severe form of network congestion, can be caused unexpectedly in a network-on-chip (NoC) due to the immanent spatio-temporal unevenness of application traffic. Hotspots reduce the NoC's effective throughput, where in the worst-case scenario, network traffic flows can be frozen indefinitely. To alleviate this problematic phenomenon several adaptive routing algorithms employ online load-balancing schemes, aiming to reduce the possibility of hotspots arising. Since most are not explicitly hotspotagnostic, they cannot completely prevent hotspot formation(s) as their reactive capability to hotspots is merely passive. This paper presents a pro-active Hotspot-Preventive Routing Algorithm (HPRA) which uses the advance knowledge gained from network embedded artificial neural network-based (ANN) hotspot predictors to guide packet routing in mitigating any unforeseen near-future hotspot occurrences. First, these ANN-based predictors are trained offline and during multicore operation they gather online statistical data to predict about-to-be-formed hotspots, promptly informing HPRA to take appropriate hotspot-preventive action(s). Next, in a holistic approach, additional ANN training is performed with data acquired after HPRA interferes, so as to further improve hotspot prediction accuracy; hence, the ANN mechanism does not only predict hotspots, but is also aware of changes that HPRA imposes upon the interconnect infrastructure. Evaluation results, including utilizing real application traffic traces gathered from parallelized workload executions onto a chip multiprocessor architecture, show that HPRA can improve network throughput up to 81 percent when compared with prior-art. Hardware synthesis results affirm the HPRA mechanism's moderate overhead requisites.

With on-chip electrical interconnects being marred by high energy-to-bandwidth costs, threatening multicore scalability, on-chip nanophotonics, which offer high throughput, yet energy-efficient communication, form an alternative attractive counterpart. In this paper we consider silicon nanophotonic components that are embedded completely within the silica (SiO2) substrate as opposed to prior-art that utilizes die on-surface silicon nanophotonics. As nanophotonic components now reside in the silica substrate's subsurface non-obstructive interconnect geometries offering higher network throughput can be implemented. First, we show using detailed simulations based on commercial optical tools that such Silicon-In-Silica (SiS) structures are feasible, derive their geometry characteristics and design parameters, and then demonstrate our proof of concept by utilizing a hybrid SiS-based photonic mesh-diagonal links network-on-chip topology. In pushing the performance envelope even more, we next develop (1) an associated contention-aware photonic adaptive routing function, and (2) a parallelized photonic channel allocation scheme, that in tandem further reduce message delivery latency. An extensive experimental evaluation, including utilizing traffic benchmarks gathered from full-system chip multiprocessor simulations, shows that our methodology boosts network throughput by up to 30.8%, reduces communication latency by up to 22.5%, and improves the throughput-to-power ratio by up to 23.7% when compared to prior-art.

With advancing process technology, Chip-Multiprocessors (CMPs) are experiencing ever worsening reliability due to prolonged operational stresses. The network-on-chip that interconnects the components of CMPs is especially vulnerable to such wearout-induced failure. To tackle this ominous threat we present Clotho, a novel, wearout-aware routing algorithm. Clotho continuously considers the stresses the on-chip interconnect experiences at runtime, along with temperature and fabrication process variation metrics, steering traffic away from locations that are most prone to Electromigration (EM)- and Hot-Carrier Injection (HCI)-induced wear. Under realistic workloads Clotho yields 66% and 8% average increases in mean time to failure for EM and HCI, respectively.

On-chip electrical links exhibit large energy-to-bandwidth costs, whereas on-chip nanophotonics, which attain high throughput, yet energy-efficient communication, have emerged as an alternative interconnect in multicore chips. Here we consider silicon nanophotonic components that are embedded completely within the silica (SiO2) substrate as opposed to existing die on-surface silicon nanophotonics. As nanophotonic components now reside subsurface, within the silica substrate, non-obstructive interconnect geometries offering higher network throughput can be implemented. First, we show using detailed simulations based on commercial tools that such Silicon-in-Silica (SiS) structures are feasible, and then demonstrate our proof of concept by utilizing a SiS-based mesh-interconnected topology with augmented diagonal optical channels that provides both higher effective throughput and throughput-to-power ratio versus prior-art.

Chip-multiprocessors are facing worsening reliability due to prolonged operational stresses, with their tile-interconnecting Network-on-Chip (NoC) being especially vulnerable to wearout-induced failure. To tackle this ominous threat we present a novel wear-aware routing algorithm that continuously considers the stresses the NoC experiences at runtime, along with temperature and fabrication process variation metrics, steering traffic away from locations that are most prone to Electromigration (EM)- and Hot-Carrier Injection (HCI)-induced wear. Under realistic applications our wear-aware algorithm yields 66% and 8% average increases in mean-time-to-failure for EM and HCI, respectively.

Moore's Law scaling continues to yield higher transistor density with each succeeding process generation, leading to today's many-core chip multiprocessors (CMPs) with tens or even hundreds of interconnected cores or tiles. Unfortunately, deep submicron CMOS process technology is marred by increasing susceptibility to wear. Prolonged operational stress gives rise to accelerated wearout and failure due to several physical failure mechanisms, including hot-carrier injection (HCI) and negative-bias temperature instability (NBTI). Each failure mechanism correlates with different usage-based stresses, all of which can eventually generate permanent faults. While the wearout of an individual core in many-core CMPs may not necessarily be catastrophic, a single fault in the interprocessor network-on-chip (NoC) fabric could render the entire chip useless, as it could lead to protocol-level deadlocks, or even partition away vital components such as the memory controller or other critical I/O. In this article, we study HCI- and NBTI-induced wear due to actual stresses caused by real workloads, applied onto the interconnect microarchitecture and develop a critical path model for NBTI-induced wearout. A key finding of this modeling is that, counter to prevailing wisdom, wearout in the CMP's on-chip interconnect is correlated with lack of load observed in the NoC routers rather than high load. We then develop a novel wearout-decelerating scheme in which routers under low load have their wear-sensitive components exercised without significantly impacting cycle time, pipeline depth, area, or power consumption of the overall router. A novel deterministic approach is proposed for the generation of appropriate exercise-mode data, ensuring design parameter targets are met. We subsequently show that the proposed design yields an ∼2,300× decrease in the rate of wear.

With increasing core counts in Chip Multi-Processor (CMP) designs, the size of the on-chip communication fabric and shared Last-Level Caches (LLC), which we term uncore here, is also growing, consuming as much as 30% of die area and a significant portion of chip power budget. In this work, we focus on improving the uncore energy-efficiency using dynamic voltage and frequency scaling. Previous approaches are mostly restricted to reactive techniques, which may respond poorly to abrupt workload and uncore utility changes. We find, however, there are predictable patterns in uncore utility which point towards the potential of a proactive approach to uncore power management. In this work, we utilize artificial intelligence principles to proactively leverage uncore utility pattern prediction via an Artificial Neural Network (ANN). ANNs, however, require training to produce accurate predictions. Architecting an efficient training mechanism without a priori knowledge of the workload is a major challenge. We propose a novel technique in which a simple Proportional Integral (PI) controller is used as a secondary classifier during ANN training, dynamically pulling the ANN up by its bootstraps to achieve accurate predictions. Both the ANN and the PI controller, then, work in tandem once the ANN training phase is complete. The advantage of using a PI controller to initially train the ANN is a dramatic acceleration of the ANN's initial learning phase. Thus, in a real system, this scenario allows quick power-control adaptation to rapid application phase changes and context switches during execution. We show that the proposed technique produces results comparable to those of pure offline training without a need for prerecorded training sets. Full system simulations using the PARSEC benchmark suite show that the bootstrapped ANN improves the energy-delay product of the uncore system by 27% versus existing state-of-the-art methodologies.

Networks-on-Chips (NoCs) are experiencing escalating susceptibility to wear-out and reduced reliability, with the risk of becoming the key point of failure in an entire multicore chip. Aiming towards seamless NoC operation in the presence of faulty communication links, in this paper we propose Hermes, a highly-robust, distributed and lightweight fault-tolerant routing algorithm, whose performance degrades gracefully with increasing faulty link counts. Hermes is a deadlock-free hybrid routing algorithm, utilizing load-balancing routing on fault-free paths to sustain high-performance, while providing pre-reconfigured escape path selection in the vicinity of faults. Additionally, Hermes identifies non-communicating network partitions in scenarios where faulty links are topologically densely distributed. An extensive experimental evaluation, including utilizing traffic benchmarks gathered from full-system chip multi-processor simulations, shows that Hermes improves network throughput by up to 3× when compared against prior-art.

Networks-on-Chips (NoCs) are experiencing escalating susceptibility to wear-out and reduced reliability, with the risk of becoming the key point of failure in an entire multicore chip. In this paper we propose Hermes, a highly-robust, distributed fault-tolerant routing algorithm, whose performance degrades gracefully with increasing faulty NoC link counts. Hermes is a deadlock-free hybrid routing algorithm, utilizing load-balanced routing on fault-free paths, while providing pre-reconfigured escape routes in the vicinity of faults. An initial experimental evaluation shows that Hermes improves network throughput by up to 2.2× when compared against the existing state-of-the-art.

Moore's Law scaling is continuing to yield even higher transistor density with each succeeding process generation, leading to today's multi-core Chip Multi-Processors (CMPs) with tens or even hundreds of interconnected cores or tiles. Unfortunately, deep sub-micron CMOS process technology is marred by increasing susceptibility to wearout. Prolonged operational stress gives rise to accelerated wearout and failure, due to several physical failure mechanisms, including Hot Carrier Injection (HCI) and Negative Bias Temperature Instability (NBTI). Each failure mechanism correlates with different usage-based stresses, all of which can eventually generate permanent faults. While the wearout of an individual core in many-core CMPs may not necessarily be catastrophic for the system, a single fault in the inter-processor Network-on-Chip (NoC) fabric could render the entire chip useless, as it could lead to protocol-level deadlocks, or even partition away vital components such as the memory controller or other critical I/O. In this paper, we develop critical path models for HCI- and NBTI-induced wear due to the actual stresses caused by real workloads, applied onto the interconnect microarchitecture. A key finding from this modeling being that, counter to prevailing wisdom, wearout in the CMP on-chip interconnect is correlated with lack of load observed in the NoC routers, rather than high load. We then develop a novel wearout-decelerating scheme in which routers under low load have their wearout-sensitive components exercised, without significantly impacting cycle time, pipeline depth, area or power consumption of the overall router. We subsequently show that the proposed design yields a 13.8x-65x increase in CMP lifetime.

The inherent spatio-temporal unevenness of traffic flows in Networks-on-Chips (NoCs) can cause unforeseen, and in cases, severe forms of congestion, known as hotspots. Hotspots reduce the NoC's effective throughput, where in the worst case scenario, the entire network can be brought to an unrecoverable halt as a hotspot(s) spreads across the topology. To alleviate this problematic phenomenon several adaptive routing algorithms employ online load-balancing functions, aiming to reduce the possibility of hotspots arising. Most, however, work passively, merely distributing traffic as evenly as possible among alternative network paths, and they cannot guarantee the absence of network congestion as their reactive capability in reducing hotspot formation(s) is limited. In this paper we present a new pro-active Hotspot-Preventive Routing Algorithm (HPRA) which uses the advance knowledge gained from network-embedded Artificial Neural Network-based (ANN) hotspot predictors to guide packet routing across the network in an effort to mitigate any unforeseen near-future occurrences of hotspots. These ANNs are trained offline and during multicore operation they gather online buffer utilization data to predict about-to-be-formed hotspots, promptly informing the HPRA routing algorithm to take appropriate action in preventing hotspot formation(s). Evaluation results across two synthetic traffic patterns, and traffic benchmarks gathered from a chip multiprocessor architecture, show that HPRA can reduce network latency and improve network throughput up to 81% when compared against several existing state-of-the-art congestion-aware routing functions. Hardware synthesis results demonstrate the efficacy of the HPRA mechanism.

Hotspots are network-on-chip (NoC) routers or modules in multicore systems which occasionally receive packetized data from other networked element producers at a rate higher than they can consume it. This adverse phenomenon may greatly reduce the performance of NoCs, especially when wormhole flow-control is employed, as backpressure can cause the buffers of neighboring routers to quickly fill-up leading to a spatial spread in congestion. This can cause the network to saturate prematurely where in the worst scenario the NoC may be rendered unrecoverable. Thus, a hotspot prevention mechanism can be greatly beneficial, as it can potentially enable the interconnection system to adjust its behavior and prevent the rise of potential hotspots, subsequently sustaining NoC performance. The inherent unevenness of traffic patterns in an NoC-based general-purpose multicore system such as a chip multiprocessor, due to the diverse and unpredictable access patterns of applications, produces unexpected hotspots whose appearance cannot be known a priori, as application demands are not predetermined, making hotspot prediction and subsequently prevention difficult. In this paper, we present an artificial neural network-based (ANN) hotspot prediction mechanism that can be potentially used in tandem with a hotspot avoidance or congestion-control mechanism to handle unforeseen hotspot formations efficiently. The ANN uses online statistical data to dynamically monitor the interconnect fabric, and reactively predicts the location of an about to-be-formed hotspot(s), allowing enough time for the multicore system to react to these potential hotspots. Evaluation results indicate that a relatively lightweight ANN-based predictor can forecast hotspot formation(s) with an accuracy ranging from 65% to 92%.

The rapid scaling of silicon technology has enabled massive transistor integration densities. Nanometer feature sizes, however, are marred by increasing variability and susceptibility to wear-out. Billion-transistor designs, such as chip multiprocessors (CMPs), are especially vulnerable to defects. CMPs rely on a network-on-chip for all their communication needs. A single link failure within this on-chip fabric can impede, halt, or even deadlock, intertile communication, which can render the entire chip multiprocessor useless. In this paper, we present a technique capable of handling very large numbers of permanent wire failures that occur in parallel links either at manufacture-time or at runtime (dynamically). As opposed to marking an entire parallel link as faulty, whenever some wires fail, the proposed methodology employs these partially-faulty links (PFLs) to continue the transfer of information-albeit at a gracefully degraded mode-in order to maintain network connectivity. Furthermore, the presented technique can designate PFLs as fully-faulty when several wires fail, by utilizing appropriate routing algorithms that bypass nonoperational links, while still maintaining load-balance in the vicinity of PFLs. The proposed scheme employs architectural support within the on-chip routers to detect link failures and enable reconfiguration at the granularity of individual wires. Hardware synthesis confirms the low-cost nature of the proposed architecture, and full-system simulations using both synthetic network traffic and real workloads demonstrate its efficacy.

Router microarchitecture plays a central role in the performance of networks-on-chip (NoCs). Buffers are needed in routers to house incoming flits that cannot be immediately forwarded due to contention. This buffering can be done at the inputs or the outputs of a router, corresponding to an input-buffered router (IBR) or an output-buffered router (OBR). OBRs are attractive because they can sustain higher throughputs and have lower queuing delays under high loads than IBRs. However, a direct implementation of an OBR requires a router speedup equal to the number of ports, making such a design prohibitive under aggressive clocking needs and limited power budgets of most NoC applications. In this paper, a new router design based on a distributed shared-buffer (DSB) architecture is proposed that aims to practically emulate an OBR. The proposed architecture introduces innovations to address the unique constraints of NoCs, including efficient pipelining and novel flow control. Practical DSB configurations are also presented with reduced power overheads while exhibiting negligible performance degradation. Compared to a state-of-the-art pipelined IBR, the proposed DSB router achieves up to 19% higher throughput on synthetic traffic and reduces packet latency on average by 61% when running SPLASH-2 benchmarks with high contention. On average, the saturation throughput of DSB routers is within 7% of the theoretically ideal saturation throughput under the synthetic workloads evaluated.

Silicon technology scaling is continuously enabling denser integration capabilities. However, this comes at the expense of higher variability and susceptibility to wear-out. With an escalating number of on-chip components expected to be defective in near-future chips, modern parallel systems, such as Chip Multi-Processors (CMP), become especially vulnerable to these faults. Just a single link failure in the underlying Network on-Chip (NoC) may cause inter-tile communication to halt and even deadlock, rendering the chip useless. While fault-tolerant routing schemes do exist, they can only handle a finite number of link faults. In this paper, we address permanent wire failures which can occur in on-chip parallel links at manufacture-time or while in operation. Instead of marking the entire link as faulty, we present a methodology where the Partially Faulty Link (PFL) can still be used to transfer data between NoC routers, thus maintaining network connectivity, extending the yield and lifetime of the chip, and allowing for graceful performance degradation. To achieve this, we devise architectural augmentations both to the router and link micro-architectures, along with link fault detection, diagnosis, and re-configuration at the level of wire granularity. Statistical link-level fault models present the usability of PFLs, while relevant load-balancing routing algorithms and low-cost re-transmission mechanisms are also presented and coupled to the proposed architecture. Hardware synthesis demonstrates the feasibility of the proposed extensions to the base NoC architecture. Results obtained from full-system simulations show that high-performance NoCs are realizable in the presence of PFLs.

Router microarchitecture plays a central role in the performance of an on-chip network (NoC). Buffers are needed in routers to house incoming flits which cannot be immediately forwarded due to contention. This buffering can be done at the inputs or the outputs of a router, corresponding to an input-buffered router (IBR) or an output-buffered router (OBR). OBRs are attractive because they can sustain higher throughputs and have lower queuing delays under high loads than IBRs. However, a direct implementation of an OBR requires a router speedup equal to the number of ports, making such a design prohibitive under aggressive clocking needs and limited power budgets of most NoC applications. In this paper, we propose a new router design that aims to emulate an OBR practically, based on a distributed shared-buffer (DSB) router architecture. We introduce innovations to address the unique constraints of NoCs, including efficient pipelining and novel flow-control. We also present practical DSB configurations that can reduce the power overhead with negligible degradation in performance. The proposed DSB router achieves up to 19% higher throughput on synthetic traffic and reduces packet latency by 60% on average for SPLASH-2 benchmarks with high contention, compared to a state-of-art pipelined IBR. On average, the saturation throughput of DSB routers is within 10% of the theoretically ideal saturation throughput under the synthetic workloads evaluated.

Interconnection networks have been deployed as the communication fabric in a wide spectrum of parallel computer systems, ranging from chip multiprocessors (CMPs) and embedded multicore systems-on-a-chip (SoCs) to clusters and server blades. Recent technology trends have permitted a rapid growth of chip resources, faster clock rates, and wider communication bandwidths, however, these trends have also led to an increase in power consumption that is becoming a key limiting factor in the design of such scalable interconnected systems. Power-aware networks, therefore, need to become inherent components of single and multi-chip parallel systems. In the hardware arena, recent interconnection network power-management research work has employed limited-scope techniques that mostly focus on reducing the power consumed by the network communication links. As these limited-scope techniques are not tailored to the applications running on the network, power savings and the corresponding impact on network latency vary significantly from one application to the next as we demonstrate in this paper; in many cases, network performance can severely suffer. In the software arena, extensive research on compile-time optimizations has produced parallelizing compilers that can efficiently map an application onto hardware for high performance. However, research into power-aware parallelizing compilers is in its infancy. In this paper, we take the first steps toward tailoring applications' communication needs at run-time for low power. We propose software techniques that extend the flow of a parallelizing compiler in order to direct run-time network power reduction. We target network links, a significant power consumer in these systems, allowing dynamic voltage scaling (DVS) instructions extracted during static compilation to orchestrate link voltage and frequency transitions for power savings during application run-time. Concurrently, an online hardware mechanism measures network congestion levels and adapts these off-line DVS settings to maximize network performance. Our simulations over three existing parallel systems, ranging from very fine-grained single-chip to coarse-grained multi-chip architectures, show that link power consumption can be reduced by up to 76.3%, with a minor increase in latency, ranging from 0.18 to 6.78% across a number of benchmark suites.

With power consumption becoming increasingly critical in interconnected systems, power-aware networks become part-and-parcel of many single-chip and multichip systems. As communication links consume significant power regardless of utilization, a mechanism to realize such power-aware networks is on/off links-network links that can be turned on/off as a function of traffic. In this paper, we investigate and propose self-regulating power-aware interconnection networks that turn their links on/off in response to bursts and dips in traffic in a distributed fashion. We explore the design space of such on/off networks, outlining a 5-step design methodology along with various building block solutions at each step that can be effectively assembled to develop various on/off network designs. We applied our methodology to the design of two classes of on/off networks with links that possess substantially different on/off delays, an on-chip network as well as a chip-to-chip network, and show that our designs are able to adapt dynamically to variations in network traffic. Three specific network designs are then constructed, presented, and evaluated. Our simulations show that link power consumption can be reduced by up to 54.4 percent, with a modest increase in network latency

Technology trends are driving parallel on-chip architectures in the form of multiprocessor systems-on-a-chip (MPSoCs) and chip multiprocessors (CMPs). In these systems, the increasing on-chip communication demand among the computation elements necessitates the use of scalable, high-bandwidth network-on-chip (NoC) fabrics instead of dedicated interconnects and shared buses. As transistor feature sizes are further miniaturized, more complicated NoC architectures become feasible that can support more demanding applications. Given the myriad emerging software-hardware combinations, for cost-effectiveness, a system designer critically needs to prune this widening NoC design-space to predict the interconnect fabric(s) that best balance(s) cost/performance, before the actual design process begins. This prompted us to develop Polaris, a system-level roadmapping toolchain for on-chip interconnection networks that helps designers predict the most suitable interconnection network design(s) tailored to their performance needs and power/silicon area constraints with respect to a range of applications that the system will run. Polaris explores the plethora of NoC designs based on projections of network traffic, architectures, and process characteristics. While Polaris's toolchain is extensible so new traffic, network designs, and technology processes can be added, the current version already incorporates 7872 NoC design points. Polaris is rapid, efficiently iterating over thousands of NoC design points, while maintaining high relative and absolute accuracies when validated against detailed NoC synthesis results.

Technology trends have led to the advent of multi-core chips in the form of both general-purpose chip multiprocessors (CMPs)and embedded multi-processor systems-on-a-chip (MPSoCs), with on-chip networks increasingly becoming the defacto communication fabric between cores as the demand for on-chip bandwidth scales up. These multi-core chips are composed of two key subcomponents: processor cores and a network fabric. Rapid, early-stage power estimation of these multi-core chips is crucial in assisting compilers in determining the most efficient thread partitioning and place-ment. While prior work in high-level power analysis exists, the focus has been on uniprocessor cores and ignores the interactions between cores via the on-chip network, as well as the power contribution of the on-chip fabric itself. In this paper we propose a ?rst high-level power analysis framework that synergistically considers both computation and communication in a complete CMP system. Processor cores and the communication fabric are both abstracted as network nodes and links, so data dependencies, structural dependencies and communication dependencies are all modeled as resource contention, with resource utilization as a proxy for relative power. Our tool has been validated against the cycle-accurate BTL simulator of the MITRawCMP, showing an average speed up of 7X while achieving relative accuracy of 9.1%. We see this as a ?rst step towards enabling the implementation of parallelizing compilers that explore various power-performance tradeoffs for future multi-core chips

Technology trends are driving parallel on-chip architectures in the form of multi-processor systems-on-a-chip (MPSoCs) and chip multi-processors (CMPs). In these systems the increasing on-chip communication demand among the computation elements necessitates the use of scalable, nigh-bandwidth network-on-chip (NoC) fabrics. As transistor feature sizes are further miniaturized leading to rapidly increasing amounts of on-chip resources, more complicated and powerful NoC architectures become feasible that can support more sophisticated and demanding applications. Given the myriad emerging software-hardware combinations, for cost-effectiveness, a system designer critically needs to prune this widening NoC design space to identify the architecture(s) that best balance(s) cost/performance, before the actual design process begins. This prompted us to develop Polaris 1, a system-level roadmap for on-chip interconnection networks that guides designers towards the most suitable network design(s) tailored to their performance needs and power/silicon area constraints with respect to a range of applications that will run over this network(s). Polaris explores the plethora of NoC designs based on projections of network traffic, architectures, and process characteristics. While the Polaris roadmapping toolchain is extensible so new traffic, network designs, and processes can be added, the current version of the roadmap already incorporates 7,872 NoC design points. Polaris is rapid and iterates over all these NoC architectures within a tractable run time of 125 hours on a typical desktop machine, while maintaining high relative and absolute accuracies when validated against detailed NoC synthesis results.

Network traffic modeling is a critical first step towards understanding and unraveling network power/performancerelated issues. Extensive prior research in the area of classic networks such as the Internet, Ethernet, and wireless LANs transporting TCP/IP, HTTP, and FTP traffic among others, has demonstrated how traffic models and model-based synthetic traffic generators can facilitate understanding of traffic characteristics and drive early-stage simulation to explore a large network design space. Though on-chip networks (a.k.a networks-on-chip (NoCs)) are becoming the de-facto scalable communication fabric in many-core systems-on-a-chip (SoCs) and chip multiprocessors (CMPs), no on-chip network traffic model that captures both spatial and temporal variations of traffic has been demonstrated yet. As available on-chip resources increase with technology scaling, enabling a myriad of new network architectures, NoCs need to be designed from the application’s perspective. In this paper we propose such an empirically-derived network on-chip traffic model for homogeneous NoCs. Our comprehensive model is based on three statistical parameters described with a 3-tuple, and captures the spatio-temporal characteristics of NoC traffic accurately with less than 5% error when compared to actual NoC application traces gathered from fullsystem simulations of three different chip platforms. We illustrate two potential uses of our traffic model: how it allows us to characterize and gain insights on NoC traffic patterns, and how it can be used to generate synthetic traffic traces that can drive NoC design-space exploration.

Interconnection networks have been deployed as the communication fabric in a wide range of parallel computer systems. With recent technological trends allowing growing quantities of chip resources and faster clock rates, there have been prevailing concerns of increasing power consumption being a major limiting factor in the design of parallel computer systems, from multiprocessor SoCs to multi-chip embedded systems and parallel servers. To tackle this, power-aware networks must become inherent components of single-chip and multi-chip system.On the hardware design side, while there has been some recent interconnection network power reduction research, especially targeted towards communication links, the techniques presented are ad hoc and are not tailored to the application running on the network. We show that with these ad hoc techniques, power savings and corresponding impact on network latency vary significantly from one application to the next -- in many cases network performance can suffer severely. On the software side, extensive research on compile-time optimization has produced parallelizing compilers that can efficiently map an application onto hardware for high performance. However, research into power-aware parallelizing compilers is in its infancy; none addressed communication power.In this paper, we take the first steps towards tailoring applications' communication needs at run-time for low power. We propose software techniques that extend the flow of a parallelizing compiler in order to direct run-time network power optimization. We target network links, the dominant power consumer in these systems, allowing DVS instructions extracted during static compilation to orchestrate link voltage and frequency transitions for power savings during application runtime. Concurrently, a hardware online mechanism measures network congestion levels and adapts these off-line DVS settings to optimize network performance. Our simulations show that link power consumption can be greatly reduced by up to 76.3%, with a minor increase in network latency in the range of 0.23 to 6.78% across a number of benchmark suites running on three existing parallel architectures, from very fine-grained single-chip to coarse-grained multi-chip architectures.

With power a major limiting factor in the design of scalable interconnected systems, power-aware networks become inherent components of single-chip and multi-chip systems. As communication links consume significant power regardless of utilization, we propose and investigate power-aware networks whose links are turned on and off in response to bursts and dips in traffic. We explore the design space of such on/off networks, outlining a 5-step design methodology along with solutions at each step that can form the building blocks of numerous designs. Two specific designs targeting links with substantially different on/off times are then presented and evaluated. Our simulations show that up to 54.4% power savings can be achieved along with at most 7.5% increase in latency.