Design and implementation of secured agent based NoC using shortest path routing algorithm

Network on chip (NoC) is a scalable interconnection architecture for every increasing communication demand between many processing cores in system on chip design. Reliability aspects are becoming an important issue in fault tolerant architecture. Hence there is a demand for fault tolerant Agent architecture with suitable routing algorithm which plays a vital role in order to enhance the NoC performance. The proposed fault tolerant Agent based NoC method is used to enhance the reliability and performance of the Multiprocessor System on Chip (MPSoC) design against faulty links and nodes. These agents are placed in hierarchical manner to collect, process, classify and distribute different fault information related to the faulty links and nodes of the network. This fault information is used for further packet routing in the network with the help of shortest path routing algorithm. In addition to this the agent will provide the security for the node by setting firewall, which then decides whether the packet has to be processed or not. This intern provides high performance, low latency NoC by avoiding deadlock and live lock with low area overhead

Secure Network-on-Chip Against Black Hole and Tampering Attacks

The Network-on-Chip (NoC) has become the communication heart of Multiprocessors-System-on-Chip (MPSoC). Therefore, it has been subject to a plethora of security threats to degrade the system performance or steal sensitive information. Due to the globalization of the modern semiconductor industry, many different parties take part in the hardware design of the system. As a result, the NoC could be infected with a malicious circuit, known as a Hardware Trojan (HT), to leave a back door for security breach purposes. HTs are smartly designed to be too small to be uncovered by offline circuit-level testing, so the system requires an online monitoring to detect and prevent the HT in runtime. This dissertation focuses on HTs inside the router of a NoC designed by a third party. It explores two HT-based threat models for the MPSoC, where the NoC experiences packet-loss and packet-tampering once the HT in the infected router is activated and is in the attacking state. Extensive experiments for each proposed architecture were conducted using a cycle-accurate simulator to demonstrate its effectiveness on the performance of the NoC-based system. The first threat model is the Black Hole Router (BHR) attack, where it silently discards the packets that are passing through without further announcement. The effect of the BHR is presented and analyzed to show the potency of the attack on a NoC-based system. A countermeasure protocol is proposed to detect the BHR at runtime and counteract the deliberate packet-dropping attack with a 26.9% area overhead, an average 21.31% performance overhead and a 22% energy consumption overhead. The protocol is extended to provide an efficient and power-gated scheme to enhance the NoC throughput and reduce the energy consumption by using end-to-end (e2e) approach. The power-gated e2e technique locates the BHR and avoids it with a 1% performance overhead and a 2% energy consumption overhead. The second threat model is a packet-integrity attack, where the HT tampers with the packet to apply a denial-of-service attack, steal sensitive information, gain unauthorized access, or misroute the packet to an unintended node. An authentic and secure NoC platform is proposed to detect and countermeasure the packet-tampering attack to maintain data-integrity and authenticity while keeping its secrecy with a 24.21% area overhead. The proposed NoC architecture is not only able to detect the attack, but also locates the infected router and isolates it from the network

Magic-State Functional Units: Mapping and Scheduling Multi-Level Distillation Circuits for Fault-Tolerant Quantum Architectures

Quantum computers have recently made great strides and are on a long-term path towards useful fault-tolerant computation. A dominant overhead in fault-tolerant quantum computation is the production of high-fidelity encoded qubits, called magic states, which enable reliable error-corrected computation. We present the first detailed designs of hardware functional units that implement space-time optimized magic-state factories for surface code error-corrected machines. Interactions among distant qubits require surface code braids (physical pathways on chip) which must be routed. Magic-state factories are circuits comprised of a complex set of braids that is more difficult to route than quantum circuits considered in previous work [1]. This paper explores the impact of scheduling techniques, such as gate reordering and qubit renaming, and we propose two novel mapping techniques: braid repulsion and dipole moment braid rotation. We combine these techniques with graph partitioning and community detection algorithms, and further introduce a stitching algorithm for mapping subgraphs onto a physical machine. Our results show a factor of 5.64 reduction in space-time volume compared to the best-known previous designs for magic-state factories.Comment: 13 pages, 10 figure

Memory and information processing in neuromorphic systems

A striking difference between brain-inspired neuromorphic processors and current von Neumann processors architectures is the way in which memory and processing is organized. As Information and Communication Technologies continue to address the need for increased computational power through the increase of cores within a digital processor, neuromorphic engineers and scientists can complement this need by building processor architectures where memory is distributed with the processing. In this paper we present a survey of brain-inspired processor architectures that support models of cortical networks and deep neural networks. These architectures range from serial clocked implementations of multi-neuron systems to massively parallel asynchronous ones and from purely digital systems to mixed analog/digital systems which implement more biological-like models of neurons and synapses together with a suite of adaptation and learning mechanisms analogous to the ones found in biological nervous systems. We describe the advantages of the different approaches being pursued and present the challenges that need to be addressed for building artificial neural processing systems that can display the richness of behaviors seen in biological systems.Comment: Submitted to Proceedings of IEEE, review of recently proposed neuromorphic computing platforms and system

E-QED: Electrical Bug Localization During Post-Silicon Validation Enabled by Quick Error Detection and Formal Methods

During post-silicon validation, manufactured integrated circuits are extensively tested in actual system environments to detect design bugs. Bug localization involves identification of a bug trace (a sequence of inputs that activates and detects the bug) and a hardware design block where the bug is located. Existing bug localization practices during post-silicon validation are mostly manual and ad hoc, and, hence, extremely expensive and time consuming. This is particularly true for subtle electrical bugs caused by unexpected interactions between a design and its electrical state. We present E-QED, a new approach that automatically localizes electrical bugs during post-silicon validation. Our results on the OpenSPARC T2, an open-source 500-million-transistor multicore chip design, demonstrate the effectiveness and practicality of E-QED: starting with a failed post-silicon test, in a few hours (9 hours on average) we can automatically narrow the location of the bug to (the fan-in logic cone of) a handful of candidate flip-flops (18 flip-flops on average for a design with ~ 1 Million flip-flops) and also obtain the corresponding bug trace. The area impact of E-QED is ~2.5%. In contrast, deter-mining this same information might take weeks (or even months) of mostly manual work using traditional approaches

Protocol-directed trace signal selection for post-silicon validation

Due to the increasing complexity of modern digital designs using NoC (network-on-chip) communication, post-silicon validation has become an arduous task that consumes much of the development time of the product. The process of finding the root cause of bugs during post-silicon validation is very difficult because of the lack of observability of all signals on the chip. To increase observability for post-silicon validation, an effective silicon debug technique is to use an on-chip trace buffer to monitor and capture the circuit response of certain selected signals during its post-silicon operation. However, because of area limitations for debug structures on chip and routing concerns, the signals that are selected to be traced are a very small subset of all available signals. Traditionally, these trace signals were chosen manually by system designers who determined what signals may be needed for debug once the design reaches post-silicon. However, because modern digital designs have become very complex with many concurrent processes, this method is no longer reliable. Recent work has concentrated on automating the selection of low-level signals from a gate-level analysis. But none of them has ever been able to interpret the trace signals as high-level meaningful debugging information. In this work, we present an automated protocol-directed trace selection where the guiding force is the set of system-level protocols. We use a probabilistic formulation to select messages for tracing and then further analyze these solutions. This method produces traces that allow a debugger to observe when behavior has deviated from the correct path of execution and localize this incorrect behavior for further analysis. Most importantly, unlike the previous gate-level analysis based methods, this method can be applied during the chip design phase when most of the debug features are also designed. In addition, this method drastically reduces the time needed to select signals, as we automate a currently manual process

Cross-layer fault tolerance in networks-on-chip

The design of Networks-on-Chip follows the Open Systems Interconnection (OSI) reference model. The OSI model defines strictly separated network abstraction layers and specifies their functionality. Each layer has layer-specific information about the network that can be exclusively accessed by the methods of the layer. Adhering to the strict layer boundaries, however, leads to methods of the individual layers working in isolation from each other. This lack of interaction between methods is disadvantageous for fault diagnosis and fault tolerance in Networks-on-Chip as it results in solutions that have a high effort in terms of the time and implementation costs required to deal with faults. For Networks-on-Chip cross-layer design is considered as a promising method to remedy these shortcomings. It removes the strict layer boundaries by the exchange of information between layers. This interaction enables methods of different layers to cooperate, and thus, deal with faults more efficiently. Furthermore, providing lower layer information to the software allows hardware methods to be implemented as software tasks resulting in a reduction of the hardware complexity. The goal of this dissertation is the investigation of cross-layer design for fault diagnosis and fault tolerance in Networks-on-Chip. For fault diagnosis a scheme is proposed that allows the interaction of protocol-based diagnosis of the transport layer with functional diagnosis of the network layer and structural diagnosis of the physical layer by exchanging diagnostic information. The techniques use this information for optimizing their own diagnosis process. For protocol-based diagnosis on the transport layer, a diagnosis protocol is proposed that is able to locate faulty links, switches, and crossbar connections. For this purpose, the technique utilizes available information of lower layers. As proof of concept for the proposed interaction scheme, the diagnosis protocol is combined with a functional and a structural diagnosis approach and the performance and diagnosis quality of the resulting combinations is investigated. The results show that the combinations of the diagnosis protocol with one of the lower layer techniques have a considerably reduced fault localization latency compared to the functional and the structural standalone techniques. This reduction, however, comes at the expense of a reduced diagnosis quality. In terms of fault tolerance, the focus of this dissertation is on the design and implementation of cross-layer approaches utilizing software methods to provide fault tolerance for network layer routings. Two approaches for different routings are presented. The requirements to provide information of lower layers to the software using the available Network-on-Chip resources and interfaces for data communication are discussed. The concepts of two mechanisms of the data link layer are presented for converting status information into communicable units and for preventing communication resources from being blocked. In the first approach, software-based packet rerouting is proposed. By incorporating information from different layers, this approach provides fault tolerance for deterministic network layer routings. As specialization of software-based rerouting, dimension-order XY rerouting is presented. In the second approach, a reconfigurable routing for Networks-on-Chip with logical hierarchy is proposed in which cross-layer interaction is used to enable hierarchical units to manage themselves autonomously and to reconfigure the routing. Both approaches are evaluated regarding their performance as well as their implementation costs. In a final study, the cross-layer diagnosis technique and cross-layer fault tolerance approaches are combined. The information obtained by the diagnosis technique is used by the fault tolerance approaches for packet rerouting or for routing reconfiguration. The combinations are evaluated regarding their impact on Networks-on-Chip performance. The results show that the crosslayer information exchange with software has a considerable impact on performance when the amount of information becomes too large. In case of crosslayer diagnosis, however, the impact on Networks-on-Chip performance is significantly lower compared to functional and structural diagnosis