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

Using Proportional-Integral-Differential approach for Dynamic Traffic Prediction in Wireless Network-on-Chip

The massive integration of cores in multi-core system has enabled chip designer to design systems while meeting the power performance demands of the applications. Wireless interconnection has emerged as an energy efficient solution to the challenges of multi-hop communication over the wireline paths in conventional Networks-on-Chips (NoCs). However, to ensure the full benefits of this novel interconnect technology, design of simple, fair and efficient Medium Access Control (MAC) mechanism to grant access to the on-chip wireless communication channel is needed. Moreover, to adapt to the varying traffic demands from the applications running on a multicore environment, MAC mechanisms should dynamically adjust the transmission slots of the wireless interfaces (WIs). To ensure an efficient utilization of the wireless medium in a Wireless NoC (WiNoC), in this work we present the design of prediction model that is used by two dynamic MAC mechanism to predict the traffic demand of the WIs and respond accordingly by adjusting transmission slots of the WIs. Through system level simulations, we show that the traffic aware MAC mechanisms are more energy efficient as well as capable of sustaining higher data bandwidth in WiNoCs

Global Congestion and Fault Aware Wireless Interconnection Framework for Multicore Systems

Multicore processors are getting more common in the implementation of all type of computing demands, starting from personal computers to the large server farms for high computational demanding applications. The network-on-chip provides a better alternative to the traditional bus based communication infrastructure for this multicore system. Conventional wire-based NoC interconnect faces constraints due to their long multi-hop latency and high power consumption. Furthermore high traffic generating applications sometimes creates congestion in such system further degrading the systems performance. In this thesis work, a novel two-state congestion aware wireless interconnection framework for network chip is presented. This WiNoC system was designed to able to dynamically redirect traffic to avoid congestion based on network condition information shared among all the core tiles in the system. Hence a novel routing scheme and a two-state MAC protocol is proposed based on a proposed two layer hybrid mesh-based NoC architecture. The underlying mesh network is connected via wired-based interconnect and on top of that a shared wireless interconnect framework is added for single-hop communication. The routing scheme is non-deterministic in nature and utilizes the principles from existing dynamic routing algorithms. The MAC protocol for the wireless interface works in two modes. The first is data mode where a token-based protocol is utilized to transfer core data. And the second mode is the control mode where a broadcast-based communication protocol is used to share the network congestion information. The work details the switching methodology between these two modes and also explain, how the routing scheme utilizes the congestion information (gathered during the control mode) to route data packets during normal operation mode. The proposed work was modeled in a cycle accurate network simulator and its performance were evaluated against traditional NoC and WiNoC designs

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

Facilitating Internet of Things on the Edge

The evolution of electronics and wireless technologies has entered a new era, the Internet of Things (IoT). Presently, IoT technologies influence the global market, bringing benefits in many areas, including healthcare, manufacturing, transportation, and entertainment. Modern IoT devices serve as a thin client with data processing performed in a remote computing node, such as a cloud server or a mobile edge compute unit. These computing units own significant resources that allow prompt data processing. The user experience for such an approach relies drastically on the availability and quality of the internet connection. In this case, if the internet connection is unavailable, the resulting operations of IoT applications can be completely disrupted. It is worth noting that emerging IoT applications are even more throughput demanding and latency-sensitive which makes communication networks a practical bottleneck for the service provisioning. This thesis aims to eliminate the limitations of wireless access, via the improvement of connectivity and throughput between the devices on the edge, as well as their network identification, which is fundamentally important for IoT service management. The introduction begins with a discussion on the emerging IoT applications and their demands. Subsequent chapters introduce scenarios of interest, describe the proposed solutions and provide selected performance evaluation results. Specifically, we start with research on the use of degraded memory chips for network identification of IoT devices as an alternative to conventional methods, such as IMEI; these methods are not vulnerable to tampering and cloning. Further, we introduce our contributions for improving connectivity and throughput among IoT devices on the edge in a case where the mobile network infrastructure is limited or totally unavailable. Finally, we conclude the introduction with a summary of the results achieved