FPSA: A Full System Stack Solution for Reconfigurable ReRAM-based NN Accelerator Architecture

Neural Network (NN) accelerators with emerging ReRAM (resistive random access memory) technologies have been investigated as one of the promising solutions to address the \textit{memory wall} challenge, due to the unique capability of \textit{processing-in-memory} within ReRAM-crossbar-based processing elements (PEs). However, the high efficiency and high density advantages of ReRAM have not been fully utilized due to the huge communication demands among PEs and the overhead of peripheral circuits. In this paper, we propose a full system stack solution, composed of a reconfigurable architecture design, Field Programmable Synapse Array (FPSA) and its software system including neural synthesizer, temporal-to-spatial mapper, and placement & routing. We highly leverage the software system to make the hardware design compact and efficient. To satisfy the high-performance communication demand, we optimize it with a reconfigurable routing architecture and the placement & routing tool. To improve the computational density, we greatly simplify the PE circuit with the spiking schema and then adopt neural synthesizer to enable the high density computation-resources to support different kinds of NN operations. In addition, we provide spiking memory blocks (SMBs) and configurable logic blocks (CLBs) in hardware and leverage the temporal-to-spatial mapper to utilize them to balance the storage and computation requirements of NN. Owing to the end-to-end software system, we can efficiently deploy existing deep neural networks to FPSA. Evaluations show that, compared to one of state-of-the-art ReRAM-based NN accelerators, PRIME, the computational density of FPSA improves by 31x; for representative NNs, its inference performance can achieve up to 1000x speedup.Comment: Accepted by ASPLOS 201

Reverse Engineering of Short Circuit Analyses

The electrical distribution system has evolved with embedded computer systems that can better manage the electrical fault that occurred around the feeders. Such random events can affect the reliability indices of overall systems. Computerized management system for distribution operation has been improving with the advanced sensing technologies. The general research question is here to articulate is the responsiveness for utility crew to pinpoint the exact location of a fault based on the SCADA fault indicators from pole-mounted feeder remote terminal units (FRTUs). This has been a tricky question because it relies on the information received from the sensors that can conclude fault with logic\u27s of over currents. The merit of this work can benefit at large the grid reliability because of time-saving in searching the exact location of a fault. The main contribution of this thesis is to utilize the 3-phase unbalanced power flow method to incrementally search for narrowing the localization of electrical short circuits. This is known as the reversal of the typical short circuit approach where a location of the fault is presumed. The 3 topological configurations of simulation studied in this thesis exhibit the typical radial configuration of a distribution feeder have been researched based on unidirectional and bidirectional power flow simulation. The exact fault location is carried in two steps. Firstly, a bisection search algorithm has been employed. Secondly, an incremental adjustment to match the simulated currents of fault with the measurements is conducted. Finally, the sensitivity analysis of a search can be improved with the proposed algorithm that leads to matching of telemetered and calculated values. The analysis of exact fault location is carried in unidirectional and bidirectional flow of power. Distributed energy resources (DER) such as residential PV at a household level as well the wind energy changes affect the protective relaying within a feeder as well as the reconfigurability of the switching sequences. Furthermost, the bidirectionality of power flow in an unbalanced manner would also be a challenging issue to deal with the power quality in automation. Finally, the simulation results based on unidirectional and bidirectional power flow are extensively discussed along with the future scope

A Scalable and Adaptive Network on Chip for Many-Core Architectures

In this work, a scalable network on chip (NoC) for future many-core architectures is proposed and investigated. It supports different QoS mechanisms to ensure predictable communication. Self-optimization is introduced to adapt the energy footprint and the performance of the network to the communication requirements. A fault tolerance concept allows to deal with permanent errors. Moreover, a template-based automated evaluation and design methodology and a synthesis flow for NoCs is introduced

Analysis of Various Decentralized Load Balancing Techniques with Node Duplication

Experience in parallel computing is an increasingly necessary skill for today’s upcoming computer scientists as processors are hitting a serial execution performance barrier and turning to parallel execution for continued gains. The uniprocessor system has now reached its maximum speed limit and, there is very less scope to improve the speed of such type of system. To solve this problem multiprocessor system is used, which have more than one processor. Multiprocessor system improves the speed of the system but it again faces some problems like data dependency, control dependency, resource dependency and improper load balancing. So this paper presents a detailed analysis of various decentralized load balancing techniques with node duplication to reduce the proper execution time

On Dynamic Monitoring Methods for Networks-on-Chip

Rapid ongoing evolution of multiprocessors will lead to systems with hundreds of processing cores integrated in a single chip. An emerging challenge is the implementation of reliable and efficient interconnection between these cores as well as other components in the systems. Network-on-Chip is an interconnection approach which is intended to solve the performance bottleneck caused by traditional, poorly scalable communication structures such as buses. However, a large on-chip network involves issues related to congestion problems and system control, for instance. Additionally, faults can cause problems in multiprocessor systems. These faults can be transient faults, permanent manufacturing faults, or they can appear due to aging. To solve the emerging traffic management, controllability issues and to maintain system operation regardless of faults a monitoring system is needed. The monitoring system should be dynamically applicable to various purposes and it should fully cover the system under observation. In a large multiprocessor the distances between components can be relatively long. Therefore, the system should be designed so that the amount of energy-inefficient long-distance communication is minimized. This thesis presents a dynamically clustered distributed monitoring structure. The monitoring is distributed so that no centralized control is required for basic tasks such as traffic management and task mapping. To enable extensive analysis of different Network-on-Chip architectures, an in-house SystemC based simulation environment was implemented. It allows transaction level analysis without time consuming circuit level implementations during early design phases of novel architectures and features. The presented analysis shows that the dynamically clustered monitoring structure can be efficiently utilized for traffic management in faulty and congested Network-on-Chip-based multiprocessor systems. The monitoring structure can be also successfully applied for task mapping purposes. Furthermore, the analysis shows that the presented in-house simulation environment is flexible and practical tool for extensive Network-on-Chip architecture analysis.Siirretty Doriast

Investigation of domestic level EV chargers in the Distribution Network: An Assessment and mitigation solution

This research focuses on the electrification of the transport sector. Such electrification could potentially pose challenges to the distribution system operator (DSO) in terms of reliability, power quality and cost-effective implementation. This thesis contributes to both, an Electrical Vehicle (EV) load demand profiling and advanced use of reactive power compensation (D-STATCOM) to facilitate flexible and secure network operation. The main aim of this research is to investigate the planning and operation of low voltage distribution networks (LVDN) with increasing electrical vehicles (EVs) proliferation and the effects of higher demand charging systems. This work is based on two different independent strands of research. Firstly, the thesis illustrates how the flexibility and composition of aggregated EVs demand can be obtained with very limited information available. Once the composition of demand is available, future energy scenarios are analysed in respect to the impact of higher EVs charging rates on single phase connections at LV distribution network level. A novel planning model based on energy scenario simulations suitable for the utilization of existing assets is developed. The proposed framework can provide probabilistic risk assessment of power quality (PQ) variations that may arise due to the proliferation of significant numbers of EVs chargers. Monte Carlo (MC) based simulation is applied in this regard. This probabilistic approach is used to estimate the likely impact of EVs chargers against the extreme-case scenarios. Secondly, in relation to increased EVs penetration, dynamic reactive power reserve management through network voltage control is considered. In this regard, a generic distribution static synchronous compensator (D-STATCOM) model is adapted to achieve network voltage stability. The main emphasis is on a generic D-STATCOM modelling technique, where each individual EV charging is considered through a probability density function that is inclusive of dynamic D-STATCOM support. It demonstrates how optimal techniques can consider the demand flexibility at each bus to meet the requirement of network operator while maintaining the relevant steady state and/or dynamic performance indicators (voltage level) of the network. The results show that reactive power compensation through D-STATCOM, in the context of EVs integration, can provide continuous voltage support and thereby facilitate 90% penetration of network customers with EV connections at a normal EV charging rate (3.68 kW). The results are improved by using optimal power flow. The results suggest, if fast charging (up to 11 kW) is employed, up to 50% of network EV customers can be accommodated by utilising the optimal planning approach. During the case study, it is observed that the transformer loading is increased significantly in the presence of D-STATCOM. The transformer loading reaches approximately up to 300%, in one of the contingencies at 11 kW EV charging, so transformer upgrading is still required. Three-phase connected DSTATCOM is normally used by the DSO to control power quality issues in the network. Although, to maintain voltage level at each individual phase with three-phase connected device is not possible. So, single-phase connected D-STATCOM is used to control the voltage at each individual phase. Single-phase connected D-STATCOM is able maintain the voltage level at each individual phase at 1 p.u. This research will be of interest to the DSO, as it will provide an insight to the issues associated with higher penetration of EV chargers, present in the realization of a sustainable transport electrification agenda

Technical solutions for low-voltage microgrid concept

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The Modeling and Advanced Controller Design of Wind, PV and Battery Inverters

Renewable energies such as wind power and solar energy have become alternatives to fossil energy due to the improved energy security and sustainability. This trend leads to the rapid growth of wind and Photovoltaic (PV) farm installations worldwide. Power electronic equipments are commonly employed to interface the renewable energy generation with the grid. The intermittent nature of renewable and the large scale utilization of power electronic devices bring forth numerous challenges to system operation and design. Methods for studying and improving the operation of the interconnection of renewable energy such as wind and PV are proposed in this Ph.D. dissertation.;A multi-objective controller including is proposed for PV inverter to perform voltage flicker suppression, harmonic reduction and unbalance compensation. A novel supervisory control scheme is designed to coordinate PV and battery inverters to provide high quality power to the grid. This proposed control scheme provides a comprehensive solution to both active and reactive power issues caused by the intermittency of PV energy. A novel real-time experimental method for connecting physical PV panel and battery storage is proposed, and the proposed coordinated controller is tested in a Hardware in the Loop (HIL) experimental platform based on Real Time Digital Simulator (RTDS).;This work also explores the operation and controller design of a microgrid consisting of a direct drive wind generator and a battery storage system. A Model Predictive Control (MPC) strategy for the AC-DC-AC converter of wind system is derived and implemented to capture the maximum wind energy as well as provide desired reactive power. The MPC increases the accuracy of maximum wind energy capture as well as minimizes the power oscillations caused by varying wind speed. An advanced supervisory controller is presented and employed to ensure the power balance while regulating the PCC bus voltage within acceptable range in both grid-connected and islanded operation.;The high variability and uncertainty of renewable energies introduces unexpected fast power variation and hence the operation conditions continuously change in distribution networks. A three-layers advanced optimization and intelligent control algorithm for a microgrid with multiple renewable resources is proposed. A Dual Heuristic Programming (DHP) based system control layer is used to ensure the dynamic reliability and voltage stability of the entire microgrid as the system operation condition changes. A local layer maximizes the capability of the Photovoltaic (PV), wind power generators and battery systems, and a Model Predictive Control (MPC) based device layer increases the tracking accuracy of the converter control. The detail design of the proposed SWAPSC scheme are presented and tested on an IEEE 13 node feeder with a PV farm, a wind farm and two battery-based energy storage systems