An Artificial Neural Networks based Temperature Prediction Framework for Network-on-Chip based Multicore Platform

Continuous improvement in silicon process technologies has made possible the integration of hundreds of cores on a single chip. However, power and heat have become dominant constraints in designing these massive multicore chips causing issues with reliability, timing variations and reduced lifetime of the chips. Dynamic Thermal Management (DTM) is a solution to avoid high temperatures on the die. Typical DTM schemes only address core level thermal issues. However, the Network-on-chip (NoC) paradigm, which has emerged as an enabling methodology for integrating hundreds to thousands of cores on the same die can contribute significantly to the thermal issues. Moreover, the typical DTM is triggered reactively based on temperature measurements from on-chip thermal sensor requiring long reaction times whereas predictive DTM method estimates future temperature in advance, eliminating the chance of temperature overshoot. Artificial Neural Networks (ANNs) have been used in various domains for modeling and prediction with high accuracy due to its ability to learn and adapt. This thesis concentrates on designing an ANN prediction engine to predict the thermal profile of the cores and Network-on-Chip elements of the chip. This thermal profile of the chip is then used by the predictive DTM that combines both core level and network level DTM techniques. On-chip wireless interconnect which is recently envisioned to enable energy-efficient data exchange between cores in a multicore environment, will be used to provide a broadcast-capable medium to efficiently distribute thermal control messages to trigger and manage the DTM schemes

A Survey of Prediction and Classification Techniques in Multicore Processor Systems

In multicore processor systems, being able to accurately predict the future provides new optimization opportunities, which otherwise could not be exploited. For example, an oracle able to predict a certain application\u27s behavior running on a smart phone could direct the power manager to switch to appropriate dynamic voltage and frequency scaling modes that would guarantee minimum levels of desired performance while saving energy consumption and thereby prolonging battery life. Using predictions enables systems to become proactive rather than continue to operate in a reactive manner. This prediction-based proactive approach has become increasingly popular in the design and optimization of integrated circuits and of multicore processor systems. Prediction transforms from simple forecasting to sophisticated machine learning based prediction and classification that learns from existing data, employs data mining, and predicts future behavior. This can be exploited by novel optimization techniques that can span across all layers of the computing stack. In this survey paper, we present a discussion of the most popular techniques on prediction and classification in the general context of computing systems with emphasis on multicore processors. The paper is far from comprehensive, but, it will help the reader interested in employing prediction in optimization of multicore processor systems

Artificial Neural Network Based Prediction Mechanism for Wireless Network on Chips Medium Access Control

As per Moore’s law, continuous improvement over silicon process technologies has made the integration of hundreds of cores on to a single chip possible. This has resulted in the paradigm shift towards multicore and many-core chips where, hundreds of cores can be integrated on the same die and interconnected using an on-chip packet-switched network called a Network-on-Chip (NoC). Various tasks running on different cores generate different rates of communication between pairs of cores. This lead to the increase in spatial and temporal variation in the workloads, which impact the long distance data communication over multi-hop wire line paths in conventional NoCs. Among different alternatives, due to the CMOS compatibility and energy-efficiency, low-latency wireless interconnects operating in the millimeter wave (mm-wave) band is nearer term solution to this multi-hop communication problem in traditional NoCs. This has led to the recent exploration of millimeter-wave (mm-wave) wireless technologies in wireless NoC architectures (WiNoC). In a WiNoC, the mm-wave wireless interconnect is realized by equipping some NoC switches with an wireless interface (WI) that contains an antenna and transceiver circuit tuned to operate in the mm-wave frequency. To enable collision free and energy-efficient communication among the WIs, the WIs is also equipped with a medium access control mechanism (MAC) unit. Due to the simplicity and low-overhead implementation, a token passing based MAC mechanism to enable Time Division Multiple Access (TDMA) has been adopted in many WiNoC architectures. However, such simple MAC mechanism is agnostic of the demand of the WIs. Based on the tasks mapped on a multicore system the demand through the WIs can vary both spatially and temporally. Hence, if the MAC is agnostic of such demand variation, energy is wasted when no flit is transferred through the wireless channel. To efficiently utilize the wireless channel, MAC mechanisms that can dynamically allocate token possession period of the WIs have been explored in recent time for WiNoCs. In the dynamic MAC mechanism, a history-based prediction is used to predict the bandwidth demand of the WIs to adjust the token possession period with respect to the traffic variation. However, such simple history based predictors are not accurate and limits the performance gain due to the dynamic MACs in a WiNoC. In this work, we investigate the design of an artificial neural network (ANN) based prediction methodology to accurately predict the bandwidth demand of each WI. Through system level simulation, we show that the dynamic MAC mechanisms enabled with the ANN based prediction mechanism can significantly improve the performance of a WiNoC in terms of peak bandwidth, packet energy and latency compared to the state-of-the-art dynamic MAC mechanisms

Head-of-Line Blocking Reduction in Power-Efficient Networks-on-Chip

Tesis por compendioNowadays, thanks to the continuous improvements in the integration scale, more and more cores are added on the same chip, leading to higher system performance. In order to interconnect all nodes, a network-on-chip (NoC) is used, which is in charge of delivering data between cores. However, increasing the number of cores leads to a significant power consumption increase, leading the NoC to be one of the most expensive components in terms of power. Because of this, during the last years, several mechanisms have been proposed to address the NoC power consumption by means of DVFS (Dynamic Voltage and Frequency Scaling) and power-gating strategies. Nevertheless, improvements achieved by these mechanisms are achieved, to a greater or lesser extent, at the cost of system performance, potentially increasing the risk of saturating the network by forming congested points which, in turn, compromise the rest of the system functionality. One side effect is the creation of the "Head-of-Line blocking" effect where congested packets at the head of queues prevent other non-blocked packets from advancing. To address this issue, in this thesis, on one hand, we propose novel congestion control techniques in order to improve system performance by removing the "Head-of-Line" blocking effect. On the other hand, we propose combined solutions adapted to DVFS in order to achieve improvements in terms of performance and power. In addition to this, we propose a path-aware power-gating-based mechanism, which is capable of detecting the flows sharing buffer resources along data paths and perform to switch them off when not needed. With all these combined solutions we can significantly reduce the power consumption of the NoC when compared with state-of-the-art proposals.Hoy en día, gracias a las mejoras en la escala de integración cada vez se integran más y más núcleos en un mismo chip, mejorando así sus prestaciones. Para interconectar todos los nodos dentro del chip se emplea una red en chip (NoC, Network-on-Chip), la cual es la encargada de intercambiar información entre núcleos. No obstante, aumentar el número de núcleos en el chip también conlleva a su vez un importante incremento en el consumo de la NoC, haciendo que ésta se convierta en una de las partes más caras del chip en términos de consumo. Por ello, en los últimos años se han propuesto diversas técnicas de ahorro de energía orientadas a reducir el consumo de la NoC mediante el uso de DVFS (Dynamic Voltage and Frequency Scaling) o estrategias basadas en "power-gating". Sin embargo, éstas mejoras de consumo normalmente se obtienen a costa de sacrificar, en mayor o menor medida, las prestaciones del sistema, aumentado potencialmente así el riesgo de saturar la red, generando puntos de congestión que, a su vez, comprometen el rendimiento del resto del sistema. Un efecto colateral es el "Head-of-Line blocking", mediante el que paquetes congestionados en la cabeza de la cola impiden que otros paquetes no congestionados avancen. Con el fin de solucionar este problema, en ésta tesis, en primer lugar, proponemos técnicas novedosas de control de congestión para incrementar el rendimiento del sistema mediante la eliminación del "Head-of-Line blocking", mientras que, por otra parte, proponemos soluciones combinadas adaptadas a DVFS con el fin de conseguir mejoras en términos de rendimiento y energía. Además, proponemos una técnica de "power-gating" orientada a rutas de datos, la cual es capaz de detectar flujos de datos compartiendo recursos a lo largo de rutas y apagar dichos recursos de forma dinámica cuando no son necesarios. Con todas éstas soluciones combinadas podemos reducir el consumo de energía de la NoC en comparación con otras técnicas presentes en el estado del arte.Hui en dia, gr\`acies a les millores en l'escala d'integraci\'o, cada vegada s'integren m\'es i m\'es nuclis en un mateix xip, la qual cosa millora les seues prestacions. Per tal d'interconectar tots els nodes dins el xip es fa \'us d'una Xarxa en Xip (NoC; Network-on-Chip), la qual \'es l'encarregada d'intercanviar informaci\'o entre els nuclis. No obstant aix\`o, incrementar el nombre de nuclis en el xip tamb\'e comporta un important augment en el consum de la NoC, la qual cosa fa que aquesta es convertisca en una de les parts m\'es costoses del xip en termes de consum. Per aix\`o, en els \'ultims anys s'han proposat diverses t\`ecniques d'estalvi d'energia orientades a reduir el consum de la NoC mitjançant l'\'us de DVFS (Dynamic Voltage and Frequency Scaling) o estrat\`egies basades en ``power-gating''. Malgrat aix\`o, aquestes millores en les prestacions normalment s'obtenen a costa de sacrificar, en major o menor mesura, les prestacions del sistema i augmenta aix\'i el risc de saturar la xarxa al generar-se punts de congesti\'o, que al mateix temps, comprometen el rendiment de la resta del sistema. Un efecte col-lateral \'es el ``Head-of- Line blocking'', mitjançant el qual, els paquets congestionats al cap de la cua, impedixen que altres paquets no congestionats avancen. A fi de solucionar eixe problema, en aquesta tesi, en primer lloc, proposem noves t\`ecniques de control de congesti\'o amb l'objectiu d'incrementar el rendiment del sistema per mitj\`a de l'eliminaci\'o del ``Head-of- Line blocking'', i d'altra banda, proposem solucions combinades adaptades a DVFS amb la finalitat d'aconseguir millores en termes de rendiment i energia. A m\'es, proposem una t\`ecnica de ``power-gating'' orientada a rutes de dades, la qual \'es capa\c c de detectar fluxos de dades al compartir recursos al llarg de les rutes i apagar eixos recursos de forma din\`amica quan no s\'on necessaris. Amb totes aquestes solucions combinades podem reduir el consum d'energia de la NoC en comparaci\'o amb altres t\`ecniques presents en l'estat de l'art.Escamilla López, JV. (2017). Head-of-Line Blocking Reduction in Power-Efficient Networks-on-Chip [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/90419TESISCompendi

Combined Dynamic Thermal Management Exploiting Broadcast-Capable Wireless Network-on-Chip Architecture

With the continuous scaling of device dimensions, the number of cores on a single die is constantly increasing. This integration of hundreds of cores on a single die leads to high power dissipation and thermal issues in modern Integrated Circuits (ICs). This causes problems related to reliability, timing violations and lifetime of electronic devices. Dynamic Thermal Management (DTM) techniques have emerged as potential solutions that mitigate the increasing temperatures on a die. However, considering the scaling of system sizes and the adoption of the Network-on-Chip (NoC) paradigm to serve as the interconnection fabric exacerbates the problem as both cores and NoC elements contribute to the increased heat dissipation on the chip. Typically, DTM techniques can either be proactive or reactive. Proactive DTM techniques, where the system has the ability to predict the thermal profile of the chip ahead of time are more desirable than reactive DTM techniques where the system utilizes thermal sensors to determine the current temperature of the chip. Moreover, DTM techniques either address core or NoC level thermal issues separately. Hence, this thesis proposes a combined proactive DTM technique that integrates both core level and NoC level DTM techniques. The combined DTM mechanism includes a dynamic temperature-aware routing approach for the NoC level elements, and includes task reallocation heuristics for the core level elements. On-chip wireless interconnects recently envisioned to enable energy-efficient data exchange between cores in a multicore chip will be used to provide a broadcast-capable medium to efficiently distribute thermal control messages to trigger and manage the DTM. Combining the proactive DTM technique with on-chip wireless interconnects, the on-chip temperature is restricted within target temperatures without significantly affecting the performance of the NoC based interconnection fabric of the multicore chip