Considering Power Variations of DVS Processing Elements for Energy Minimisation in Distributed Systems

Dynamic voltage scaling (DVS) is a powerful technique to reduce power dissipation in embedded systems. Some efficient DVS algorithms have been recently proposed for the energy reduction in distributed system. However, they achieve the energy savings solely by scaling the system task with respect to the timing constraints, while neglecting that power varies among the tasks executed by DVS processing elements (DVS-PEs). In this paper we investigate the problem of considering DVS-PE power variations dependent on the executed tasks, during the synthesis of distributed embedded systems and its impact on the energy savings. Unlike previous approaches, which minimise the energy consumption by exploiting the available slack time without considering the PE power profiles, a new and fast heuristic for the voltage scaling problem is proposed, which improves the voltage selection for each task dependent on the individual power dissipation caused by that task. Experimental results show that energy reductions with up to 80.7% are achieved by integrating the proposed DVS algorithm, which considers the PE power profiles, into the co-synthesis of distributed systems

Dynamic and Leakage Power-Composition Profile Driven Co-Synthesis for Energy and Cost Reduction

Recent research has shown that combining dynamic voltage scaling (DVS) and adaptive body bias (ABB) techniques achieve the highest reduction in embedded systems energy dissipation [1]. In this paper we show that it is possible to produce comparable energy saving to that obtained using combined DVS and ABB techniques but with reduced hardware cost achieved by employing processing elements (PEs) with separate DVS or ABB capability. A co-synthesis methodology which is aware of tasks’ power-composition profile (the ratio of the dynamic power to the leakage power) is presented. The methodology selects voltage scaling capabilities (DVS, ABB, or combined DVS and ABB) for the PEs, maps, schedules, and voltage scales applications given as task graphs with timing constraints, aiming to dynamic and leakage energy reduction at low hardware cost. We conduct detailed experiments, including a real-life example, to demonstrate the effectiveness of our methodology. We demonstrate that it is possible to produce designs that contain PEs with only DVS or ABB technique but have energy dissipation that are only 4.4% higher when compared with the same designs that employ PEs with combined DVS and ABB capabilities

Training Spiking Neural Networks Using Lessons From Deep Learning

The brain is the perfect place to look for inspiration to develop more efficient neural networks. The inner workings of our synapses and neurons provide a glimpse at what the future of deep learning might look like. This paper serves as a tutorial and perspective showing how to apply the lessons learnt from several decades of research in deep learning, gradient descent, backpropagation and neuroscience to biologically plausible spiking neural neural networks. We also explore the delicate interplay between encoding data as spikes and the learning process; the challenges and solutions of applying gradient-based learning to spiking neural networks; the subtle link between temporal backpropagation and spike timing dependent plasticity, and how deep learning might move towards biologically plausible online learning. Some ideas are well accepted and commonly used amongst the neuromorphic engineering community, while others are presented or justified for the first time here. A series of companion interactive tutorials complementary to this paper using our Python package, snnTorch, are also made available: https://snntorch.readthedocs.io/en/latest/tutorials/index.htm

DYNAMIC VOLTAGE SCALING FOR PRIORITY-DRIVEN SCHEDULED DISTRIBUTED REAL-TIME SYSTEMS

Energy consumption is increasingly affecting battery life and cooling for real- time systems. Dynamic Voltage and frequency Scaling (DVS) has been shown to substantially reduce the energy consumption of uniprocessor real-time systems. It is worthwhile to extend the efficient DVS scheduling algorithms to distributed system with dependent tasks. The dissertation describes how to extend several effective uniprocessor DVS schedul- ing algorithms to distributed system with dependent task set. Task assignment and deadline assignment heuristics are proposed and compared with existing heuristics concerning energy-conserving performance. An admission test and a deadline com- putation algorithm are presented in the dissertation for dynamic task set to accept the arriving task in a DVS scheduled real-time system. Simulations show that an effective distributed DVS scheduling is capable of saving as much as 89% of energy that would be consumed without using DVS scheduling. It is also shown that task assignment and deadline assignment affect the energy- conserving performance of DVS scheduling algorithms. For some aggressive DVS scheduling algorithms, however, the effect of task assignment is negligible. The ad- mission test accept over 80% of tasks that can be accepted by a non-DVS scheduler to a DVS scheduled real-time system

Dynamic Power Management for Reactive Stream Processing on the SCC Tiled Architecture

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License(http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.Dynamic voltage and frequency scaling} (DVFS) is a means to adjust the computing capacity and power consumption of computing systems to the application demands. DVFS is generally useful to provide a compromise between computing demands and power consumption, especially in the areas of resource-constrained computing systems. Many modern processors support some form of DVFS. In this article we focus on the development of an execution framework that provides light-weight DVFS support for reactive stream-processing systems (RSPS). RSPS are a common form of embedded control systems, operating in direct response to inputs from their environment. At the execution framework we focus on support for many-core scheduling for parallel execution of concurrent programs. We provide a DVFS strategy for RSPS that is simple and lightweight, to be used for dynamic adaptation of the power consumption at runtime. The simplicity of the DVFS strategy became possible by sole focus on the application domain of RSPS. The presented DVFS strategy does not require specific assumptions about the message arrival rate or the underlying scheduling method. While DVFS is a very active field, in contrast to most existing research, our approach works also for platforms like many-core processors, where the power settings typically cannot be controlled individually for each computational unit. We also support dynamic scheduling with variable workload. While many research results are provided with simulators, in our approach we present a parallel execution framework with experiments conducted on real hardware, using the SCC many-core processor. The results of our experimental evaluation confirm that our simple DVFS strategy provides potential for significant energy saving on RSPS.Peer reviewe

Multi-objective network planning for the integration of electric vehicles as responsive demands

The integration of electric vehicles (EVs) into distribution networks presents substantial challenges to Distribution Network Operators (DNOs) internationally. In the 12 months from November 2017, EV registrations in Great Britain have increased by ~22% [A.1], though it is noted that EVs account for only 6% of all UK vehicle registrations [A.1] in 2018. With the UK Government announcement in 2017 [A.2] that "by 2040 there will be an end to the sale of all conventional petrol and diesel cars and vans", the penetration of EVs will require to - unless a new technology emerges - grow exponentially over the next 10 to 20 years towards 100% penetration by 2050. However, the increasing penetration of EVs can provide to the system multiple benefits and assist in mitigating issues; if EV integration is optimally planned using a suitable method. The managed charging of multiple EVs can assist in better utilising power generated by intermittent renewables, which will provide substantial benefits such as peak shifting, deferred reinforcement costs and the reduced requirement for imported energy to support the network at times of need.;Accurately assessing the impact that EVs will have on distribution networks is critical to DNOs [A.3]. In particular, the aim of this thesis is to identify the optimal location, battery size, charger power output and operational envelope for multiple EVs when used as responsive demands in high voltage/low voltage (HV/LV) distribution networks. Societal benefits can include reduced or deferred asset investment costs; reduced technical losses and increasing the utilisation of renewable generation [A.3]. System benefits must be accounted for and can support and inform planning and operational decisions - such as asset investment and network reinforcement. Coordinated smart charging of multiple EVs can assist in managing peaks in the demand curve and increase the utilisation of intermittent renewables. Unmanaged EV charging at times of peak demand would require the DNO to invest in reinforcement solutions to ensure the required additional capacity is made available. However, one approach is to cluster EV charging in periods when the base load would otherwise be low, to lessen the need for asset reinforcement as EV charging during the period of peak demand would be avoided.;Time periods for charging EVs (dependent on the chosen objectives) will be identified and then correlated to times when renewable generation availability is high and when base demand is low. The use of the presented network planning tool will identify EV charging strategies that can be applied to multiple EVs (based on the chosen objectives and with respect to constraints) whilst optimising the type, number and location on a specific modelled network. The planning framework utilises the Strength Pareto Evolutionary Algorithm 2 (SPEA2); the use of this algorithm will ensure that the network constraints are not breached and that multiple objectives are included in the analyses. This thesis investigates the impact that the inclusion of multiple EVs (when used as responsive demands); will have on the HV distribution network when the additional EV load is smartly scheduled to meet specific objectives and to correspond with the availability of intermittent renewables. The ultimate aim of this planning approach is to offer DNOs low cost solutions to multiobjective problems relating to EV integration and operation. [References A1-A3 for Abstract available p. XV of thesis.]The integration of electric vehicles (EVs) into distribution networks presents substantial challenges to Distribution Network Operators (DNOs) internationally. In the 12 months from November 2017, EV registrations in Great Britain have increased by ~22% [A.1], though it is noted that EVs account for only 6% of all UK vehicle registrations [A.1] in 2018. With the UK Government announcement in 2017 [A.2] that "by 2040 there will be an end to the sale of all conventional petrol and diesel cars and vans", the penetration of EVs will require to - unless a new technology emerges - grow exponentially over the next 10 to 20 years towards 100% penetration by 2050. However, the increasing penetration of EVs can provide to the system multiple benefits and assist in mitigating issues; if EV integration is optimally planned using a suitable method. The managed charging of multiple EVs can assist in better utilising power generated by intermittent renewables, which will provide substantial benefits such as peak shifting, deferred reinforcement costs and the reduced requirement for imported energy to support the network at times of need.;Accurately assessing the impact that EVs will have on distribution networks is critical to DNOs [A.3]. In particular, the aim of this thesis is to identify the optimal location, battery size, charger power output and operational envelope for multiple EVs when used as responsive demands in high voltage/low voltage (HV/LV) distribution networks. Societal benefits can include reduced or deferred asset investment costs; reduced technical losses and increasing the utilisation of renewable generation [A.3]. System benefits must be accounted for and can support and inform planning and operational decisions - such as asset investment and network reinforcement. Coordinated smart charging of multiple EVs can assist in managing peaks in the demand curve and increase the utilisation of intermittent renewables. Unmanaged EV charging at times of peak demand would require the DNO to invest in reinforcement solutions to ensure the required additional capacity is made available. However, one approach is to cluster EV charging in periods when the base load would otherwise be low, to lessen the need for asset reinforcement as EV charging during the period of peak demand would be avoided.;Time periods for charging EVs (dependent on the chosen objectives) will be identified and then correlated to times when renewable generation availability is high and when base demand is low. The use of the presented network planning tool will identify EV charging strategies that can be applied to multiple EVs (based on the chosen objectives and with respect to constraints) whilst optimising the type, number and location on a specific modelled network. The planning framework utilises the Strength Pareto Evolutionary Algorithm 2 (SPEA2); the use of this algorithm will ensure that the network constraints are not breached and that multiple objectives are included in the analyses. This thesis investigates the impact that the inclusion of multiple EVs (when used as responsive demands); will have on the HV distribution network when the additional EV load is smartly scheduled to meet specific objectives and to correspond with the availability of intermittent renewables. The ultimate aim of this planning approach is to offer DNOs low cost solutions to multiobjective problems relating to EV integration and operation. [References A1-A3 for Abstract available p. XV of thesis.