Showing papers on "Dynamic demand published in 2021"

Dynamic Power Allocation for Cell-Free Massive MIMO: Deep Reinforcement Learning Methods

Abstract: Power allocation plays a central role in cell-free (CF) massive multiple-input multiple-output (MIMO) systems. Many effective methods, e.g., the weighted minimum mean square error (WMMSE) algorithm, have been developed for optimizing the power allocation. Since the state of the channels evolves in time, the power allocation should stay in tune with this state. The present methods to achieve this typically find a near-optimal solution in an iterative manner at the cost of a considerable computational complexity, potentially compromising the timeliness of the power allocation. In this paper we address this problem by exploring the use of data-driven methods since they can achieve near-optimal performance with a low computational complexity. Deep reinforcement learning (DRL) is one such method. We explore two DRL power allocation methods, namely the deep Q-network (DQN) and the deep deterministic policy gradient (DDPG). The objective is to maximize the sum-spectral efficiency (SE) in CF massive MIMO, operating in the microwave domain. The numerical results, obtained for a 3GPP indoor scenario, show that the DRL-based methods have a competitive performance compared with WMMSE and the computational complexity is much lower. We found that the well-trained DRL methods achieved at least a 33% higher sum-SE than the WMMSE algorithm. The execution times of the DRL methods in our simulation platform are 0.1% of the WMMSE algorithm.

Effectiveness of demand response in achieving supply-demand matching in a renewables dominated electricity system: A modelling approach

Abstract: Globally, electricity systems are undergoing transitions from robust, carbon-intensive, and firm power conventional systems to uncertain, intermittent, and variable renewable energy integrated low carbon systems. These transitioning electricity systems have moved from a situation of “matching available supply with dynamic demand” to “matching dynamic supply with dynamic demand.” These transformations have led to several new challenges - significant mismatch in periods of high supply and high demand, the shift in the method of accessing energy resources for electricity generation from “procure, store and generate when needed” to “generate when available,” continuous struggle to match variable supply with variable demand, and installed capacity redundancy, temporal as well as permanent leading to low plant load factors. Actions on the supply-side alone will not be enough to address these challenges and achieve optimal functioning of the electricity system. We need effective demand-side solutions, too, to manage variations in both supply and demand. In this paper, it is proposed to study the effectiveness of demand-side interventions as potential solutions for managing the variabilities introduced by renewable energy mainstreaming. Towards this, we develop a mixed-integer linear programming model to implement and validate emergency and economic demand response (DR) programs. DR options like load curtailment, short-, medium- and long-term load shifting are considered with both incentive-based and penalty-based pricing strategies to influence consumer participation. The Karnataka electricity system is used as a case study for model implementation and validation. The findings suggest that DR interventions are very effective in moderating variability in electricity demand by chopping the peak loads and topping the valleys. Further, benefits include postponement of installed capacity additions, enhanced utilization of available capacity, and minimization of demand variability.

Performance Investigation of Various SRAM Cells for IoT Based Wearable Biomedical Devices

Abstract: The field of IC technology grows every day and it plays a decisive role in a day to day activity. One of the most emerging fields is biomedical. This field is getting wider and the size of the devices becomes reduced from ECG machines to portable medical devices. Nowadays wearable devices and implantable biomedical devices on the body captures the biosignals and convert it as potential signals to measure the variation and the devices which are connected to the cloud using IoT technology is helpful to digest the types of problem with less time and better accuracy. So the biopotential signal has to be shared and processed between the memory and the processor. The more sensible processor has to collect the required data from the associated memory to make an agile response. Hence this paper describes the logic and criteria on choosing the most prominent memory SRAM. The SRAM architecture in this paper is discussed based on transistors count like 5T, 6T, 8T,9T, and 10T. The no of transistors on the SRAM used to decide the level of leakage power and other junction power and also the die area it occupies. Here the 5T SRAM shows the notable variations in the delay and has a good level on leakage power and dynamic power reduction. Henceforth using the proposed SRAM in wearable biomedical devices could cause betterment in speed, agility in response, and consume less power. This SRAM can be used to access denser data with less delay time.

Power Optimization in Device-to-Device Communications: A Deep Reinforcement Learning Approach With Dynamic Reward

Abstract: Device-to-Device (D2D) communication can be used to improve system capacity and energy efficiency (EE) in cellular networks. One of the critical challenges in D2D communications is to extend network lifetime by efficient and effective resource management. Deep reinforcement learning (RL) provides a promising solution for resource management in wireless communication systems. This letter aims to maximise the EE while satisfying the system throughput constraints as well as the quality of service (QoS) requirements of D2D pairs and cellular users in an underlay D2D communication network. To achieve this, a deep RL based dynamic power optimization algorithm with dynamic rewards is proposed. Moreover, a novel algorithm with two parallel deep Q networks (DQNs) is designed to maximize the EE of the considered network. The proposed deep RL based power optimization method with dynamic rewards achieves higher EE while satisfying the system throughput requirements.

RECON: Resource-Efficient CORDIC-Based Neuron Architecture

Abstract: Contemporary hardware implementations of artificial neural networks face the burden of excess area requirement due to resource-intensive elements such as multiplier and non-linear activation functions. The present work addresses this challenge by proposing a resource-efficient Co-ordinate Rotation Digital Computer (CORDIC)-based neuron architecture (RECON) which can be configured to compute both multiply-accumulate (MAC) and non-linear activation function (AF) operations. The CORDIC-based architecture uses linear and trigonometric relationships to realize MAC and AF operations respectively. The proposed design is synthesized and verified at 45nm technology using Cadence Virtuoso for all physical parameters. Implementation of the signed fixed-point 8-bit MAC using our design, shows 60% less area, latency, and power product (ALP) and shows improvement by 38% in area, 27% in power dissipation, and 15% in latency with respect to the state-of-the-art MAC design. Further, Monte-Carlo simulations for process-variations and device-mismatch are performed for both the proposed model and the state-of-the-art to evaluate expectations of functions of randomness in dynamic power variation. The dynamic power variation for our design shows that worst-case mean is $189.73\mu W$ which is 63% of the state-of-the-art.

Optimal Energy Management of a Grid-Tied Solar PV-Battery Microgrid: A Reinforcement Learning Approach

Abstract: In the near future, microgrids will become more prevalent as they play a critical role in integrating distributed renewable energy resources into the main grid. Nevertheless, renewable energy sources, such as solar and wind energy can be extremely volatile as they are weather dependent. These resources coupled with demand can lead to random variations on both the generation and load sides, thus complicating optimal energy management. In this article, a reinforcement learning approach has been proposed to deal with this non-stationary scenario, in which the energy management system (EMS) is modelled as a Markov decision process (MDP). A novel modification of the control problem has been presented that improves the use of energy stored in the battery such that the dynamic demand is not subjected to future high grid tariffs. A comprehensive reward function has also been developed which decreases infeasible action explorations thus improving the performance of the data-driven technique. A Q-learning algorithm is then proposed to minimize the operational cost of the microgrid under unknown future information. To assess the performance of the proposed EMS, a comparison study between a trading EMS model and a non-trading case is performed using a typical commercial load curve and PV profile over a 24-h horizon. Numerical simulation results indicate that the agent learns to select an optimized energy schedule that minimizes energy cost (cost of power purchased from the utility and battery wear cost) in all the studied cases. However, comparing the non-trading EMS to the trading EMS model operational costs, the latter one was found to decrease costs by 4.033% in summer season and 2.199% in winter season.

QoS-Oriented Dynamic Power Allocation in NOMA-Based Wireless Caching Networks

Abstract: Non-orthogonal multiple access (NOMA) based wireless caching network (WCN) is considered as a promising technology for next-generation wireless communications since it can significantly improve the spectral efficiency. In this letter, we propose a quality of service (QoS)-oriented dynamic power allocation strategy for NOMA-WCN. In content placement phase, base station (BS) sends multiple files to helpers by allocating different powers according to the different QoS targets of files, for ensuring that all helpers can successfully decode the two most popular files. In content delivery phase, helpers serve two users at the same time by allocating the minimum power to far user according to the QoS requirement, and then all the remaining power is allocated to near user. Hence, our proposed method is able to increase the hit probability and drop the outage probability compared with conventional methods. Simulation results confirm that the proposed power allocation method can significantly improve the caching hit probability and reduce the user outage probability.

Single-Stage Hybrid Energy Storage Integration in Electric Vehicles Using Vector Controlled Power Sharing

Abstract: The dual inverter topology driving an open-winding motor is well known in high voltage motor drive applications. This structure allows two energy sources to be directly connected to an open-winding motor. This enables the integration of supercapacitors into a battery electric vehicle (EV). Unlike existing solutions, this article demonstrates dynamic power sharing between the dual energy sources by controlling the active and reactive voltages of the twin inverters, thus enabling the use of the supercapacitor for either active power assist and/or reactive power assist. The dedicated vector-controlled power sharing method and energy management is shown to achieve power sharing in the dual inverter drive integrating a battery and supercapacitor, thereby eliminating the need for an additional cascaded dc/dc converter to extract/supply energy to the supercapacitor. It also enables improved efficiency by eliminating switching losses in the supercapacitor inverter during low power operation. The proposed voltage vector splitting method is also shown to achieve battery-to-supercapacitor power exchange for regulating the net energy in the supercapacitor without affecting motor operation. A laboratory prototype utilizing a 110-kW liquid-cooled EV motor and supercapacitor bank is developed to verify the practical implementation of the power and energy management strategy.

Model Predictive Active Power Control for Optimal Structural Load Equalization in Waked Wind Farms

Abstract: In this article, we propose a model predictive active power control (APC) enhanced by the optimal coordination of the structural loadings of wind turbines (WTs) operating with fully developed wind farm (WF) flows that have extensive interactions with the atmospheric boundary layer. In general, the APC problem, that is, distributing a WF power reference among the operating WTs, does not have a unique solution; this fact can be exploited for structural load alleviation of the individual WTs. Therefore, we formulated a constrained optimization problem to simultaneously minimize the WF power reference tracking errors and the structural load deviations of the WTs from their mean value. The wind power plant is represented by a dynamic 3-D large-eddy simulation model, whereas the predictive controller employs a simplified, computationally inexpensive model to predict the dynamic power and load responses of the turbines that experience turbulent WF flows and wakes. An adjoint approach is an efficient tool used to iteratively compute the gradient of the formulated parameter-varying optimal control problem over a finite prediction horizon. We have discussed the applicability, key features, and computational complexity of the controller by using a WF example consisting of 3x4 turbines with different wake interactions for each row. The performance of the proposed adjoint-based model predictive control for APC was evaluated by measuring power reference tracking errors and the corresponding damage equivalent fatigue loads of the WT towers; we compared our proposed control design with recently published proportional-integral-based APC approaches.

Train timetabling in rail transit network under uncertain and dynamic demand using Advanced and Adaptive NSGA-II

Abstract: It is critical to design an adaptable and stable train timetable for long-term use in rail transit network that not only meets the dynamicity of passenger demand in different hours within one day, but also meets the uncertainty of passenger demand in different days. In this study, a scenario-based train timetabling framework is constructed to classify the possibilities of passenger demand in multiple days into a set of scenarios based on profile and volume of passenger demand. On this basis, multi-scenario demand input method (MM) is introduced to deal with the uncertainty of passenger demand, which is different from one-scenario method (OM) and average-scenario method (AM). A MM-based mixed-integer linear programming model is formulated for the bi-objective train timetabling problem under uncertain and dynamic demand at acyclic network level, in which multi-scenario small-granularity passenger demand follows actual distribution processed from historical data. The two objectives are to minimize train service cost and penalized passenger waiting time from perspectives of enterprises and passengers. Advanced and Adaptive NSGA II (AANSGA-II) is proposed to cope with the high-complexity bi-objective problem, which applies advanced population sorting based on neighborhood distance, adaptive genetic operation based on scoring mechanism and improved population initialization based on boundary individuals. The model and algorithm are testified by a small-scale numerical experiment on a virtual line and a large-scale real-world instance in Shenyang Metro network. As a result, MM-based train timetables are generally better than AM-based and OM-based train timetables in reducing generalized cost and raising robustness. Besides, AANSGA-II is more applicable than NSGA-II and CPLEX in shortening computation time at the same time of improving computation result.

PCVM.ARIMA: predictive consolidation of virtual machines applying ARIMA method

Abstract: Cloud computing adopts virtualization technology, including migration and consolidation of virtual machines, to overcome resource utilization problems and minimize energy consumption. Most of the approaches have focused on minimizing the number of physical machines and rarely have devoted attention to minimizing the number of migrations. They also decide based on the current resources utilization without considering the demand for resources in the future. Some approaches minimize the number of active physical machines and Service Level Agreement (SLA) violations with the number of unnecessary migrations. They consider the current resource utilization of physical machines and neglect from demands for future resource requirements. As a result, as time passes, the number of unnecessary migrations, and subsequently, the rate of SLA violations in data centers increases. Alternatively, several approaches only focus on a hardware level and reduce the physical machine’s dynamic power consumption. The lack of control over the overload of physical machines increases the amount of violation. In this paper, a framework called PCVM.ARIMA is presented that focuses on the dynamic consolidation of virtual machines over the minimum number of physical machines, minimize the number of unnecessary migrations, detect the physical machine overloading, and SLA based on the ARIMA prediction model. Moreover, the Dynamic Voltage and Frequency Scaling (DVFS) technique is used to apply the optimal frequency to heterogeneous physical machines. The experimental results show that the presented framework significantly reduces energy consumption while it improves the QoS factors in comparison to some baseline methods.

Nonintrusive Uncertainty Quantification of Dynamic Power Systems Subject to Stochastic Excitations

Abstract: Continuous-time random disturbances (also called stochastic excitations) due to increasing renewable generation have an increasing impact on power system dynamics; However, except from the slow Monte Carlo simulation, most existing methods for quantifying this impact are intrusive , meaning they are not based on commercial simulation software and hence are difficult to use for power utility companies. To fill this gap, this paper proposes an efficient and nonintrusive method for quantifying uncertainty in dynamic power systems subject to stochastic excitations. First, the Gaussian or non-Gaussian stochastic excitations are modeled with an Ito process as stochastic differential equations. Then, the Ito process is spectrally represented by independent Gaussian random parameters, which enables the polynomial chaos expansion (PCE) of the system dynamic response to be calculated via an adaptive sparse probabilistic collocation method. Finally, the probability distribution and the high-order moments of the system dynamic response and performance index are accurately and efficiently quantified. The proposed nonintrusive method is based on commercial simulation software such as PSS/E with carefully designed input signals, which ensures ease of use for power utility companies. The proposed method is validated via case studies of IEEE 39-bus and 118-bus test systems.

Optimal Peak Shaving Control Using Dynamic Demand and Feed-In Limits for Grid-Connected PV Sources With Batteries

Abstract: Peak shaving of utility grid power is an important application, which benefits both grid operators and end users. In this article, an optimal rule-based peak shaving control strategy with dynamic demand and feed-in limits is proposed for grid-connected photovoltaic (PV) systems with battery energy storage systems. A method to determine demand and feed-in limits depending on the day-ahead predictions of load demand and PV power profiles is developed. Furthermore, an optimal rule-based control strategy that determines day-ahead charge/discharge schedules of battery for peak shaving of utility grid power is proposed. The rules are formulated such that the peak utility grid demand and feed-in powers are limited to the corresponding demand and feed-in limits of the day, respectively, while ensuring that the state-of-charge (SoC) of the battery at the end of the day is the same as the SoC of the start of the day. The optimal inputs required for applying the proposed rule-based control strategy are determined using a genetic algorithm for minimizing peak energy drawn from the utility grid. The proposed control algorithm is tested for various PV power and load demand profiles using MATLAB.

Non-Volatile RF Reconfigurable Antenna on Flexible Substrate for Wireless IoT Applications

Abstract: To date semiconductor switches are still the main enablers for electrical circuit and system reconfigurability. They however not only consume dynamic power but also dissipate static power, the former for performing on/off operation and latter for holding on/off state. These semiconductor devices are volatile and not energy efficient due to the need for holding voltage and can significantly increase the system power consumption where hundreds and thousands of switches are needed, such as in large reconfigurable intelligent surfaces and large antenna arrays. In this work, we report a non-volatile reconfigurable antenna that can switch between dual-band at 2.4 GHz and 5 GHz to a single band at 3 GHz. The measured results including reflection, gain and radiation patterns reveal promising performance, experimentally demonstrating a new approach of design and realization of RF switch integrated multi-band reconfigurable antennas. This zero-static power mechanism, along with easy fabrication on the flexible substrates would be very beneficial for Internet of Things (IoT) applications.

PSO-Based UAV Deployment and Dynamic Power Allocation for UAV-Enabled Uplink NOMA Network

Abstract: Recently, unmanned aerial vehicles (UAVs) have been used as flying base stations (BSs) to take advantage of line-of-sight (LOS) connectivity and efficiently enable fifth-generation (5G) and cellular network coverage and data rates. On the other hand, nonorthogonal multiple access (NOMA) is a promising technique to help achieve unprecedented requirements by simultaneously allowing multiple users to send data over the same resource block. In this paper, we study a UAV-enabled uplink NOMA network, where the UAV collects data from ground users while flying at a certain altitude. Unlike all existing work on this topic, this study consists of two stages. In the first stage, we use the well-known Particle Swarm Optimization (PSO) algorithm, which is a metaheuristic algorithm, to deploy the UAV in 3D space, so that the users’ sum pathlosses are minimized. In the second stage, we investigate the user pairing problem and propose a dynamic power allocation technique for determining the user’s power allocation coefficients, as well as a closed-form equation for the ergodic sum-rate. Results show our PSO-based algorithm prevailing over the Genetic Algorithm (GA) and random deployment methods. The proposed dynamic power allocation strategy maximizes the network’s ergodic sum-rate and outperforms the fixed power allocation strategy. Additionally, the results reveal that the best pairing scheme is the one that keeps uniform channel gain difference in the same pair.

Speed control and quantum vibration reduction of Brushless DC Motor using FPGA based Dynamic Power Containment Technique

Abstract: In modern industrial field Brushless DC Motor (BLDC) is important component of electromechanical energy conversion. The BLDC is used in many application like high power traction, high end pumps etc. In this BLDC motor have a drive to operate in a constant operation but some noise and losses are create, so the various control technique is used to reduce the noise although some harmonic loss is occur in existing system. In that reason a new Dynamic Power Containment Technique (DPCT) algorithm is developed to reduce the harmonic loss and increase the system efficiency. The inverter is used to drive the Brushless DC Motor (BLDC) in a constant speed operation. This proposed control system is replace to classical cascaded method in speed and current control of BLDC motor. The inverter input current is directly control with help of the proposed FPGA based DPCT control system. The proposed system performance is analysis through the MATLAB simulation software. This proposed DPCT model is given the solution of delay issue. Afterward, another immediate pay technique was proposed to foresee the adjustment in current inside the defer time. The existing comparison of two-step predictive strategy, the new method is also easier to develop and can be used for optimized control to choose the best switch state in each cycle.

Performance Study of Cognitive Relay NOMA Networks With Dynamic Power Transmission

Abstract: The cooperative non-orthogonal multiple access (NOMA) networks with one pair primary user and one pair cognitive user share the same spectrum resource via a common relay is considered in this paper. We propose a dynamic power transmission scheme for both uplink and downlink NOMA transmission in cognitive relay networks, which preserves the quality of service for the primary user. The closed-form expressions of overall outage probability and average sum rate for the proposed dynamic power transmission scheme of cognitive relay NOMA networks are derived. Both developed analytical results and Monte Carlo simulations show that the proposed dynamic power control scheme can dramatically enhance performance gain for the proposed networks, compared to other existing NOMA power allocation schemes.

A Node Role Dynamic Change Method Among Repeater, Receiver, and Decoupling Using Topology Switching in Multinode WPT System

Abstract: In the multidevice application of wireless power transfer (WPT) technology, classical method cannot balance power transfer efficiency, transfer distance, and dynamic power variation requirements. For multinode WPT system, this article proposes a flexible repeater power transmission mode characterized by node role change function. Each node role can be flexibly switched among repeater, receiver, and decoupling by using a topology switching method to satisfy dynamical repeater power transmission requirements, which is realized by a reusing topology and without additional circuit. Furthermore, the topology switching method is studied to verify that the transition is smooth in dynamic switching. Besides, this article also analyzes the system performances in different operation modes. The experimental results verify the effectiveness of the proposed method.

A solution algorithm for multi-period bi-level channel optimization with dynamic price-dependent stochastic demand

Abstract: We extend the single-period decentralized channel optimization problem with stochastic demand into a multi-period, non-autonomous one and propose an efficient general solution algorithm for it A supply channel composed of two price-setting agents in a bi-level (Stackelberg) framework has to address an uncertain dynamic demand for a perishable good at different times The stochastic demand model is general (additive-multiplicative) A class of price-dependent memory functions is embedded in the proposed representation of uncertain demand such that they carry the effect of demand level at present over to the demand in the future Due to this dependence of the current demand to the pricing history, the state space of the ensuing games becomes highly nested The proposed iterative algorithm decouples the nested equilibria into separate yet interdependent sub-problems and provides explicit solutions All the model variables and parameters are considered explicitly time-dependent enabling the model to cover cases with finite and infinite time-horizons Flexibility and generality of our solution scheme make it applicable to a wide variety of economic contexts In a series of examples we demonstrate how, fed by different market specifications and memory functions, the solution algorithm can find equilibrium results for a diverse set of scenarios at different times

A Decentralized Automatic Load Power Allocation Strategy for Hybrid Energy Storage System

Abstract: A decentralized improved I-V droop control strategy for battery-supercapacitor (SC) hybrid energy storage system (HESS) is proposed in this paper. The dynamic power sharing between battery and SC is realized by replacing the constant droop coefficient in I-V droop control with virtual impedance, i.e. virtual inductance for battery side converter and virtual resistance for SC side converter. Besides, by injecting the virtual inductance in the battery side converter, negligible DC bus voltage deviation can be achieved without extra voltage compensator. Moreover, the state-of-charge (SoC) recovery is also considered to extend the service life of the HESS. Furthermore, in the proposed regulated power system, since the power allocation, DC bus stability and SoC recovery are decoupled from each other, the design of control parameters is simple. The corresponding design guideline is demonstrated in this paper. Finally, to verify the accuracy and feasibility of the theoretical analyses, hardware in the loop simulations have been conducted.

Model Predictive Control Based Dynamic Power Loss Prediction for Hybrid Energy Storage System in DC Microgrids

Abstract: In islanding microgrids, supercapacitors (SCs) are used to compensate the transient power fluctuation caused by sudden variations of load demand and generation power to keep the output voltage stable and reduce the stress in batteries. However, SC current in dynamic response leads to transient power loss on power electronic converters, and it would cause an additional voltage ripple. To smoothen the voltage fluctuation, a dual layer model predictive control (MPC) method is proposed in this paper to control the charging/discharging behaviors efficiently. The dynamic power loss can be predicted by the predicted current and duty ratio in the primary layer MPC. The predicted dynamic power loss is one of the state variables in the secondary layer MPC to generate the more optimal power reference for the primary layer MPC, which can compensate the voltage ripple caused by dynamic power loss and reduce the dynamic error and settling time. The proposed method is validated by simulation and hardware experimental results, demonstrating significant improvements than other methods.

Study of the Intelligent Control and Modes of the Arctic-Adopted Wind–Diesel Hybrid System

Abstract: For energy supply in the Arctic regions, hybrid systems should be designed and equipped to ensure a high level of renewable energy penetration. Energy systems located in remote Arctic areas may experience many peculiar challenges, for example, due to the limited transport options throughout the year and the lack of qualified on-site maintenance specialists. Reliable operation of such systems in harsh climatic conditions requires not only a standard control system but also an advanced system based on predictions concerning weather, wind, and ice accretion on the blades. To satisfy these requirements, the current work presents an advanced intelligent automatic control system. In the developed control system, the transformation, control, and distribution of energy are based on dynamic power redistribution, dynamic control of dump loads, and a bi-directional current transducer. The article shows the architecture of the advanced control system, presents the results of field studies under the standard control approach, and models the performance of the system under different operating modes. Additionally, the effect of using turbine control to reduce the effects of icing is examined. It is shown that the advanced control approach can reduce fuel consumption in field tests by 22%. Moreover, the proposed turbine control scheme has the potential to reduce icing effects by 2% to 5%.

Cross-Layer Approximate Hardware Synthesis for Runtime Configurable Accuracy

Abstract: Approximate computing trades off computation accuracy against energy efficiency. The extent of approximation tolerance, however, significantly varies with a change in input characteristics and applications. We propose a novel cross-layer approach for the synthesis of runtime accuracy-configurable hardware that minimizes energy consumption at area expense. To that end, first, we explore instantiating multiple hardware blocks in the architecture with different fixed approximation levels. These blocks can be selected dynamically and thus allow to configure the accuracy during runtime. They benefit from having fewer transistors and also synthesis relaxations in contrast to state-of-the-art gating mechanisms that only switch off a group of paths of the circuit. Our cross-layer approach combines instantiating such blocks in the architecture with area-efficient gating mechanisms that reduce toggling activity, creating a fine-grained design-time knob on energy versus area. We present a systematic methodology to explore this joint design space and find energy–area optimal solutions as a function of required accuracies, their utilization in the workload, together with hardware parameters: dynamic power savings, area of the hardware block, and leakage of the technology. Examining total energy savings for a range of circuits under different workloads and accuracy tolerances shows that our method finds Pareto-optimal solutions providing up to 32% and 60% energy savings compared to state-of-the-art accuracy-configurable gating mechanism and an exact hardware block, respectively, at $2\times $ area cost.