Toward Reliable, Secure, and Energy-Efficient Multi-Core System Design

Computer hardware researchers have perennially focussed on improving the performance of computers while stipulating the energy consumption under a strict budget. While several innovations over the years have led to high performance and energy efficient computers, more challenges have also emerged as a fallout. For example, smaller transistor devices in modern multi-core systems are afflicted with several reliability and security concerns, which were inconceivable even a decade ago. Tackling these bottlenecks happens to negatively impact the power and performance of the computers. This dissertation explores novel techniques to gracefully solve some of the pressing challenges of the modern computer design. Specifically, the proposed techniques improve the reliability of on-chip communication fabric under a high power supply noise, increase the energy-efficiency of low-power graphics processing units, and demonstrate an unprecedented security loophole of the low-power computing paradigm through rigorous hardware-based experiments

Nature-Inspired Interconnects for Self-Assembled Large-Scale Network-on-Chip Designs

Future nano-scale electronics built up from an Avogadro number of components needs efficient, highly scalable, and robust means of communication in order to be competitive with traditional silicon approaches. In recent years, the Networks-on-Chip (NoC) paradigm emerged as a promising solution to interconnect challenges in silicon-based electronics. Current NoC architectures are either highly regular or fully customized, both of which represent implausible assumptions for emerging bottom-up self-assembled molecular electronics that are generally assumed to have a high degree of irregularity and imperfection. Here, we pragmatically and experimentally investigate important design trade-offs and properties of an irregular, abstract, yet physically plausible 3D small-world interconnect fabric that is inspired by modern network-on-chip paradigms. We vary the framework's key parameters, such as the connectivity, the number of switch nodes, the distribution of long- versus short-range connections, and measure the network's relevant communication characteristics. We further explore the robustness against link failures and the ability and efficiency to solve a simple toy problem, the synchronization task. The results confirm that (1) computation in irregular assemblies is a promising and disruptive computing paradigm for self-assembled nano-scale electronics and (2) that 3D small-world interconnect fabrics with a power-law decaying distribution of shortcut lengths are physically plausible and have major advantages over local 2D and 3D regular topologies

Novel Metric for Load Balance and Congestion Reducing in Network on-Chip

The Network-on-Chip (NoC) is an alternative pattern that is considered as an emerging technology for distributed embedded systems. The traditional use of multi-cores in computing increase the calculation performance; but affect the network communication causing congestion on nodes which therefore decrease the global performance of the NoC. To alleviate this problematic phenomenon, several strategies were implemented, to reduce or prevent the occurrence of congestion, such as network status metrics, new routing algorithm, packets injection control, and switching strategies. In this paper, we carried out a study on congestion in a 2D mesh network, through various detailed simulations. Our focus was on the most used congestion metrics in NoC. According to our experiments and performed simulations under different traffic scenarios, we found that these metrics are less representative, less significant and yet they do not give a true overview of reading within the NoC nodes at a given cycle. Our study shows that the use of other complementary information regarding the state of nodes and network traffic flow in the design of a novel metric, can really improve the results. In this paper, we put forward a novel metric that takes into account the overall operating state of a router in the design of adaptive XY routing algorithm, aiming to improve routing decisions and network performance. We compare the throughput, latency, resource utilization, and congestion occurrence of proposed metric to three published metrics on two specific traffic patterns in a varied packets injection rate. Our results indicate that our novel metric-based adaptive XY routing has overcome congestion and significantly improve resource utilization through load balancing; achieving an average improvement rate up to 40 % compared to adaptive XY routing based on the previous congestion metrics

Aging-Aware Routing Algorithms for Network-on-Chips

Network-on-Chip (NoC) architectures have emerged as a better replacement of the traditional bus-based communication in the many-core era. However, continuous technology scaling has made aging mechanisms, such as Negative Bias Temperature Instability (NBTI) and electromigration, primary concerns in NoC design. In this work, a novel system-level aging model is proposed to model the effects of aging in NoCs, caused due to (a) asymmetric communication patterns between the network nodes, and (b) runtime traffic variations due to routing policies. This work observes a critical need of a holistic aging analysis, which when combined with power-performance optimization, poses a multi-objective design challenge. To solve this problem, two different aging-aware routing algorithms are proposed: (a) congestion-oblivious Mixed Integer Linear Programming (MILP)-based routing algorithm, and (b) congestion-aware adaptive routing algorithm and router micro-architecture. After extensive experimental evaluations, proposed routing algorithms reduce aging-induced power-performance overheads while also improving the system robustness

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

STTAR: A Traffic- and Thermal-Aware Adaptive Routing for 3D Network-on-Chip Systems

Since the three-dimensional Network on Chip (3D NoC) uses through-silicon via technology to connect the chips, each silicon layer is conducted through heterogeneous thermal, and 3D NoC system suffers from thermal problems. To alleviate the seriousness of the thermal problem, the distribution of data packets usually relies on traffic information or historical temperature information. However, thermal problems in 3D NoC cannot be solved only based on traffic or temperature information. Therefore, we propose a Score-Based Traffic- and Thermal-Aware Adaptive Routing (STTAR) that applies traffic load and temperature information to routing. First, the STTAR dynamically adjusts the input and output buffer lengths of each router with traffic load information to limit routing resources in overheated areas and control the rate of temperature rise. Second, STTAR adopts a scoring strategy based on temperature and the number of free slots in the buffer to avoid data packets being transmitted to high-temperature areas and congested areas and to improve the rationality of selecting routing output nodes. In our experiments, the proposed scoring Score-Based Traffic- and Thermal-Aware Adaptive Routing (STTAR) scheme can increase the throughput by about 14.98% to 47.90% and reduce the delay by about 10.80% to 35.36% compared with the previous works