A Scalable & Energy Efficient Graphene-Based Interconnection Framework for Intra and Inter-Chip Wireless Communication in Terahertz Band

Network-on-Chips (NoCs) have emerged as a communication infrastructure for the multi-core System-on-Chips (SoCs). Despite its advantages, due to the multi-hop communication over the metal interconnects, traditional Mesh based NoC architectures are not scalable in terms of performance and energy consumption. Folded architectures such as Torus and Folded Torus were proposed to improve the performance of NoCs while retaining the regular tile-based structure for ease of manufacturing. Ultra-low-latency and low-power express channels between communicating cores have also been proposed to improve the performance of conventional NoCs. However, the performance gain of these approaches is limited due to metal/dielectric based interconnection. Many emerging interconnect technologies such as 3D integration, photonic, Radio Frequency (RF), and wireless interconnects have been envisioned to alleviate the issues of a metal/dielectric interconnect system. However, photonic and RF interconnects need the additional physically overlaid optical waveguides or micro-strip transmission lines to enable data transmission across the NoC. Several on-chip antennas have shown to improve energy efficiency and bandwidth of on-chip data communications. However, the date rates of the mm-wave wireless channels are limited by the state-of-the-art power-efficient transceiver design. Recent research has brought to light novel graphene based antennas operating at THz frequencies. Due to the higher operating frequencies compared to mm-wave transceivers, the data rate that can be supported by these antennas are significantly higher. Higher operating frequencies imply that graphene based antennas are just hundred micrometers in size compared to dimensions in the range of a millimeter of mm-wave antennas. Such reduced dimensions are suitable for integration of several such transceivers in a single NoC for relatively low overheads. In this work, to exploit the benefits of a regular NoC structure in conjunction with emerging Graphene-based wireless interconnect. We propose a toroidal folding based NoC architecture. The novelty of this folding based approach is that we are using low power, high bandwidth, single hop direct point to point wireless links instead of multihop communication that happens through metallic wires. We also propose a novel phased based communication protocol through which multiple wireless links can be made active at a time without having any interference among the transceiver. This offers huge gain in terms of performance as compared to token based mechanism where only a single wireless link can be made active at a time. We also propose to extend Graphene-based wireless links to enable energy-efficient, phase-based chip-to-chip communication to create a seamless, wireless interconnection fabric for multichip systems as well. Through cycle-accurate system-level simulations, we demonstrate that such designs with torus like folding based on THz links instead of global wires along with the proposed phase based multichip systems. We provide estimates that they are able to provide significant gains (about 3 to 4 times better in terms of achievable bandwidth, packet latency and average packet energy when compared to wired system) in performance and energy efficiency in data transfer in a NoC as well as multichip system. Thus, realization of these kind of interconnection framework that could support high data rate links in Tera-bits-per-second that will alleviate the capacity limitations of current interconnection framework

Secure Virtualization and Multicore Platforms State-of-the-Art report

SVaM

MULTI-SCALE SCHEDULING TECHNIQUES FOR SIGNAL PROCESSING SYSTEMS

A variety of hardware platforms for signal processing has emerged, from distributed systems such as Wireless Sensor Networks (WSNs) to parallel systems such as Multicore Programmable Digital Signal Processors (PDSPs), Multicore General Purpose Processors (GPPs), and Graphics Processing Units (GPUs) to heterogeneous combinations of parallel and distributed devices. When a signal processing application is implemented on one of those platforms, the performance critically depends on the scheduling techniques, which in general allocate computation and communication resources for competing processing tasks in the application to optimize performance metrics such as power consumption, throughput, latency, and accuracy. Signal processing systems implemented on such platforms typically involve multiple levels of processing and communication hierarchy, such as network-level, chip-level, and processor-level in a structural context, and application-level, subsystem-level, component-level, and operation- or instruction-level in a behavioral context. In this thesis, we target scheduling issues that carefully address and integrate scheduling considerations at different levels of these structural and behavioral hierarchies. The core contributions of the thesis include the following. Considering both the network-level and chip-level, we have proposed an adaptive scheduling algorithm for wireless sensor networks (WSNs) designed for event detection. Our algorithm exploits discrepancies among the detection accuracy of individual sensors, which are derived from a collaborative training process, to allow each sensor to operate in a more energy efficient manner while the network satisfies given constraints on overall detection accuracy. Considering the chip-level and processor-level, we incorporated both temperature and process variations to develop new scheduling methods for throughput maximization on multicore processors. In particular, we studied how to process a large number of threads with high speed and without violating a given maximum temperature constraint. We targeted our methods to multicore processors in which the cores may operate at different frequencies and different levels of leakage. We develop speed selection and thread assignment schedulers based on the notion of a core's steady state temperature. Considering the application-level, component-level and operation-level, we developed a new dataflow based design flow within the targeted dataflow interchange format (TDIF) design tool. Our new multiprocessor system-on-chip (MPSoC)-oriented design flow, called TDIF-PPG, is geared towards analysis and mapping of embedded DSP applications on MPSoCs. An important feature of TDIF-PPG is its capability to integrate graph level parallelism and actor level parallelism into the application mapping process. Here, graph level parallelism is exposed by the dataflow graph application representation in TDIF, and actor level parallelism is modeled by a novel model for multiprocessor dataflow graph implementation that we call the Parallel Processing Group (PPG) model. Building on the contribution above, we formulated a new type of parallel task scheduling problem called Parallel Actor Scheduling (PAS) for chip-level MPSoC mapping of DSP systems that are represented as synchronous dataflow (SDF) graphs. In contrast to traditional SDF-based scheduling techniques, which focus on exploiting graph level (inter-actor) parallelism, the PAS problem targets the integrated exploitation of both intra- and inter-actor parallelism for platforms in which individual actors can be parallelized across multiple processing units. We address a special case of the PAS problem in which all of the actors in the DSP application or subsystem being optimized can be parallelized. For this special case, we develop and experimentally evaluate a two-phase scheduling framework with three work flows --- particle swarm optimization with a mixed integer programming formulation, particle swarm optimization with a simulated annealing engine, and particle swarm optimization with a fast heuristic based on list scheduling. Then, we extend our scheduling framework to support general PAS problem which considers the actors cannot be parallelized

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

Architecting a One-to-many Traffic-Aware and Secure Millimeter-Wave Wireless Network-in-Package Interconnect for Multichip Systems

With the aggressive scaling of device geometries, the yield of complex Multi Core Single Chip(MCSC) systems with many cores will decrease due to the higher probability of manufacturing defects especially, in dies with a large area. Disintegration of large System-on-Chips(SoCs) into smaller chips called chiplets has shown to improve the yield and cost of complex systems. Therefore, platform-based computing modules such as embedded systems and micro-servers have already adopted Multi Core Multi Chip (MCMC) architectures overMCSC architectures. Due to the scaling of memory intensive parallel applications in such systems, data is more likely to be shared among various cores residing in different chips resulting in a significant increase in chip-to-chip traffic, especially one-to-many traffic. This one-to-many traffic is originated mainly to maintain cache-coherence between many cores residing in multiple chips. Besides, one-to-many traffics are also exploited by many parallel programming models, system-level synchronization mechanisms, and control signals. How-ever, state-of-the-art Network-on-Chip (NoC)-based wired interconnection architectures do not provide enough support as they handle such one-to-many traffic as multiple unicast trafficusing a multi-hop MCMC communication fabric. As a result, even a small portion of such one-to-many traffic can significantly reduce system performance as traditional NoC-basedinterconnect cannot mask the high latency and energy consumption caused by chip-to-chipwired I/Os. Moreover, with the increase in memory intensive applications and scaling of MCMC systems, traditional NoC-based wired interconnects fail to provide a scalable inter-connection solution required to support the increased cache-coherence and synchronization generated one-to-many traffic in future MCMC-based High-Performance Computing (HPC) nodes. Therefore, these computation and memory intensive MCMC systems need an energy-efficient, low latency, and scalable one-to-many (broadcast/multicast) traffic-aware interconnection infrastructure to ensure high-performance. Research in recent years has shown that Wireless Network-in-Package (WiNiP) architectures with CMOS compatible Millimeter-Wave (mm-wave) transceivers can provide a scalable, low latency, and energy-efficient interconnect solution for on and off-chip communication. In this dissertation, a one-to-many traffic-aware WiNiP interconnection architecture with a starvation-free hybrid Medium Access Control (MAC), an asymmetric topology, and a novel flow control has been proposed. The different components of the proposed architecture are individually one-to-many traffic-aware and as a system, they collaborate with each other to provide required support for one-to-many traffic communication in a MCMC environment. It has been shown that such interconnection architecture can reduce energy consumption and average packet latency by 46.96% and 47.08% respectively for MCMC systems. Despite providing performance enhancements, wireless channel, being an unguided medium, is vulnerable to various security attacks such as jamming induced Denial-of-Service (DoS), eavesdropping, and spoofing. Further, to minimize the time-to-market and design costs, modern SoCs often use Third Party IPs (3PIPs) from untrusted organizations. An adversary either at the foundry or at the 3PIP design house can introduce a malicious circuitry, to jeopardize an SoC. Such malicious circuitry is known as a Hardware Trojan (HT). An HTplanted in the WiNiP from a vulnerable design or manufacturing process can compromise a Wireless Interface (WI) to enable illegitimate transmission through the infected WI resulting in a potential DoS attack for other WIs in the MCMC system. Moreover, HTs can be used for various other malicious purposes, including battery exhaustion, functionality subversion, and information leakage. This information when leaked to a malicious external attackercan reveals important information regarding the application suites running on the system, thereby compromising the user profile. To address persistent jamming-based DoS attack in WiNiP, in this dissertation, a secure WiNiP interconnection architecture for MCMC systems has been proposed that re-uses the one-to-many traffic-aware MAC and existing Design for Testability (DFT) hardware along with Machine Learning (ML) approach. Furthermore, a novel Simulated Annealing (SA)-based routing obfuscation mechanism was also proposed toprotect against an HT-assisted novel traffic analysis attack. Simulation results show that,the ML classifiers can achieve an accuracy of 99.87% for DoS attack detection while SA-basedrouting obfuscation could reduce application detection accuracy to only 15% for HT-assistedtraffic analysis attack and hence, secure the WiNiP fabric from age-old and emerging attacks

Dynamic Voltage and Frequency Scaling for Wireless Network-on-Chip

Previously, research and design of Network-on-Chip (NoC) paradigms where mainly focused on improving the performance of the interconnection networks. With emerging wide range of low-power applications and energy constrained high-performance applications, it is highly desirable to have NoCs that are highly energy efficient without incurring performance penalty. In the design of high-performance massive multi-core chips, power and heat have become dominant constrains. Increased power consumption can raise chip temperature, which in turn can decrease chip reliability and performance and increase cooling costs. It was proven that Small-world Wireless Network-on-Chip (SWNoC) architecture which replaces multi-hop wire-line path in a NoC by high-bandwidth single hop long range wireless links, reduces the overall energy dissipation when compared to wire-line mesh-based NoC architecture. However, the overall energy dissipation of the wireless NoC is still dominated by wire-line links and switches (buffers). Dynamic Voltage Scaling is an efficient technique for significant power savings in microprocessors. It has been proposed and deployed in modern microprocessors by exploiting the variance in processor utilization. On a Network-on-Chip paradigm, it is more likely that the wire-line links and buffers are not always fully utilized even for different applications. Hence, by exploiting these characteristics of the links and buffers over different traffic, DVFS technique can be incorporated on these switches and wire-line links for huge power savings. In this thesis, a history based DVFS mechanism is proposed. This mechanism uses the past utilization of the wire-line links & buffers to predict the future traffic and accordingly tune the voltage and frequency for the links and buffers dynamically for each time window. This mechanism dynamically minimizes the power consumption while substantially maintaining a high performance over the system. Performance analysis on these DVFS enabled Wireless NoC shows that, the overall energy dissipation is improved by around 40% when compared Small-world Wireless NoCs

A Survey of Techniques For Improving Energy Efficiency in Embedded Computing Systems

Recent technological advances have greatly improved the performance and features of embedded systems. With the number of just mobile devices now reaching nearly equal to the population of earth, embedded systems have truly become ubiquitous. These trends, however, have also made the task of managing their power consumption extremely challenging. In recent years, several techniques have been proposed to address this issue. In this paper, we survey the techniques for managing power consumption of embedded systems. We discuss the need of power management and provide a classification of the techniques on several important parameters to highlight their similarities and differences. This paper is intended to help the researchers and application-developers in gaining insights into the working of power management techniques and designing even more efficient high-performance embedded systems of tomorrow

An IoT Endpoint System-on-Chip for Secure and Energy-Efficient Near-Sensor Analytics

Near-sensor data analytics is a promising direction for IoT endpoints, as it minimizes energy spent on communication and reduces network load - but it also poses security concerns, as valuable data is stored or sent over the network at various stages of the analytics pipeline. Using encryption to protect sensitive data at the boundary of the on-chip analytics engine is a way to address data security issues. To cope with the combined workload of analytics and encryption in a tight power envelope, we propose Fulmine, a System-on-Chip based on a tightly-coupled multi-core cluster augmented with specialized blocks for compute-intensive data processing and encryption functions, supporting software programmability for regular computing tasks. The Fulmine SoC, fabricated in 65nm technology, consumes less than 20mW on average at 0.8V achieving an efficiency of up to 70pJ/B in encryption, 50pJ/px in convolution, or up to 25MIPS/mW in software. As a strong argument for real-life flexible application of our platform, we show experimental results for three secure analytics use cases: secure autonomous aerial surveillance with a state-of-the-art deep CNN consuming 3.16pJ per equivalent RISC op; local CNN-based face detection with secured remote recognition in 5.74pJ/op; and seizure detection with encrypted data collection from EEG within 12.7pJ/op.Comment: 15 pages, 12 figures, accepted for publication to the IEEE Transactions on Circuits and Systems - I: Regular Paper