Reduce Power Consumption Research Articles

Multimodal flexible electronic skin with adjustable module size for robot collision detection

Purpose This paper aims to present a modular multimodal flexible electronic skin that can be used for robot collision detection in human–robot interactions. This type of electronic skin will meet the requirements of performance indicators such as sensing mode, sensing domain coverage and dynamic data update rate in human–robot interactions. Design/methodology/approach The electronic skin uses a modular architecture, and the sensing module is designed to be adjustable in size so that it can be easily deployed on complex robot surfaces, increasing area coverage, reducing power consumption, and improving data update rates. Findings The authors evaluated electronic skin through experiments using a UR5 robot. Electronic skin has high static scene perception differentiation and dynamic scene perception abilities. Moreover, the robot realizes a high-speed collision response based on the electronic skin proposed in this study. Originality/value The proposed electronic skin provides crucial technical support for advancing robotic technologies, and holds promising prospects for industrial applications.

Experimental Analysis of Flow Separation Control by a Dielectric Barrier Discharge Plasma Actuator in Burst-in-Burst Actuation Mode

This study investigated the effectiveness of a dielectric barrier discharge (DBD) plasma actuator operating in burst-in-burst (BIB) mode for flow separation control on a NACA 0015 airfoil. Time-resolved particle image velocimetry measurements were conducted at a Reynolds number of 66,000 and 13° angle of attack. Various BIB signal configurations were tested, with actuation periods of 70 ms and 150 ms, non-actuation periods ranging from 5 ms to 50 ms, and burst frequencies of 300 Hz and 600 Hz. Proper orthogonal decomposition was applied to analyze the flow field dynamics. The results showed that BIB actuation maintained flow attachment with reduced power consumption compared with continuous burst actuation. However, the effectiveness was highly sensitive to the BIB parameters, with some configurations failing to achieve consistent reattachment and becoming unstable. This study reveals complex interactions between actuation vortices and separation processes, highlighting both the potential and challenges of intermittent plasma actuation for efficient flow control.

Nonvolatile logic inverters based on 2D CuInP2S6 ferroelectric field effect transistors

With the capability of in-memory computing, integrated nonvolatile logic devices can mitigate the back-and-forth movement of data between storage and logic units, thus effectively enhancing computational speed and reducing power consumption. In this work, two-dimensional (2D) CuInP2S6, which reveals robust polarization within a few nanometer thicknesses, was utilized as gate dielectric for ferroelectric field effect transistor (FeFET). Device with a ReS2 channel demonstrated a high on/off ratio of current of 105, accompanied by a substantial hysteresis window of 2.8 V. Additionally, ReS2 FETs, gated with an h-BN layer, exhibited a switch ratio of 108 and minimal hysteresis of 61.6 mV at room temperature, attributed to the atomically flat heterointerface with negligible traps. Leveraging the performance of these devices enabled the creation of a nonvolatile logic inverter, wherein the ReS2/h-BN FET acts as the load transistor, and ReS2/h-BN/CIPS FeFET serves as the driving unit. This configuration stably operates with low power consumption of 0.45 μW and outstanding retention exceeding 1000 s. This work presents a viable device architecture for designing nonvolatile logic circuits applicable in in-memory computing.

An Intelligent Scheduling Approach on Mobile OS for Optimizing UI Smoothness and Power

Mobile devices need to respond quickly to diverse user inputs. The existing approaches often heuristically raise the CPU/GPU frequency according to the empirical rules when facing burst inputs and various changes. Although doing so can be effective sometimes, the existing approaches still need improvements. For instance, raising processors’ frequency can lead to high power consumption when the frequency is over-provisioned or fails to meet user demands when the frequency is under-provisioned. To this end, we propose MobiRL, a reinforcement learning-based scheduler for intelligent adjusting CPU/GPU frequency to satisfy user demands accurately on mobile systems. MobiRL monitors the mobile system status and autonomously learns to optimize UI smoothness and power consumption by conducting CPU/GPU frequency-adjusting actions. The experimental results on the latest delivered smartphones show that MobiRL outperforms the widely used commercial scheduler on real devices – reducing the frame drop rate by 4.1% and reducing power consumption by 42.8%, respectively. Moreover, compared with a study using Q-Learning for CPU frequency scheduling, MobiRL achieves up to 2.5% lower frame drop rate and reduces power consumption by 32.6%, respectively. Our approach has been deployed in mobile phone products.

Green upgrading of SPring-8 to produce stable, ultrabrilliant hard X-ray beams.

SPring-8-II is a major upgrade project of SPring-8 that was inaugurated in October 1997 as a third-generation synchrotron radiation light source. This upgrade project aims to achieve three goals simultaneously: achievement of excellent light source performance, refurbishment of aged systems, and significant reduction in power consumption for the entire facility. A small emittance of 50 pm rad will be achieved by (1) replacing the existing double-bend lattice structure with a five-bend achromat one, (2) lowering the stored beam energy from 8 to 6 GeV, (3) increasing the horizontal damping partition number from 1to 1.3, and (4) enhancing horizontal radiation damping by installing damping wigglers in long straight sections. The use of short-period in-vacuum undulators allows ultrabrilliant X-rays to be provided while keeping a high-energy spectral range even at the reduced electron-beam energy of 6 GeV. To reduce power consumption, the dedicated, aged injector system has been shut down and the high-performance linear accelerator of SACLA, a compact X-ray free-electron laser (XFEL) facility, is used as the injector of the ring in a time-shared manner. This allows the simultaneous operation of XFEL experiments at SACLA and full/top-up injection of the electron beam into the ring. This paper overviews the concept of the SPring-8-II project, the system design of the light source and the details of the accelerator component design.

Direct Current to Digital Converter (DIDC): A Current Sensor

This paper introduces a systematic approach to the design of Direct Current-to-Digital Converter (DIDC) specifically engineered to overcome the limitations of traditional current measurement methodologies in System-on-Chip (SoC) designs. The proposed DIDC addresses critical challenges such as high power consumption, large area requirements, and the need for intermediate analog signals. By incorporating a current mirror in a cascode topology and managing the current across multiple binary-sized branches with the Successive Approximation Register (SAR) logic, the design achieves precise current measurement. A simple comparator, coupled with an isolation circuit, ensures accurate and reliable sensing. Fabricated using the TSMC 180 nm process, the DIDC achieves 8-bit precision without the need for nonlinearity calibration, showcasing remarkable energy efficiency with an energy per conversion of 1.52 pJ, power consumption of 117 µW, and a compact area of 0.016 mm². This innovative approach not only reduces power consumption and area, but also provides a scalable and efficient solution for next-generation semiconductor technologies. The ability to conduct online measurements during both standard operations and in-field conditions significantly enhances the performance and reliability of SoCs, making this DIDC a promising advancement in the field.

Graphene Phase Modulators Operating in the Transparency Regime.

Next-generation data networks need to support Tb/s rates. In-phase and quadrature (IQ) modulation combine phase and intensity information to increase the density of encoded data, reduce overall power consumption by minimizing the number of channels, and increase noise tolerance. To reduce errors when decoding the received signal, intersymbol interference must be minimized. This is achieved with pure phase modulation, where the phase of the optical signal is controlled without changing its intensity. Phase modulators are characterized by the voltage required to achieve a π phase shift, Vπ, the device length, L, and their product, VπL. To reduce power consumption, IQ modulators are needed with <1 V drive voltages and compact (sub-cm) dimensions, which translate in VπL < 1Vcm. Si and LiNbO3 (LN) IQ modulators do not currently meet these requirements because VπL > 1Vcm. Here, we report a double single-layer graphene (SLG) Mach-Zehnder modulator (MZM) with pure phase modulation in the transparency regime, where optical losses are minimized and remain constant with increasing voltage. Our device has VπL ∼ 0.3Vcm, matching state-of-the-art SLG-based MZMs and plasmonic LN MZMs, but with pure phase modulation and low insertion loss (∼5 dB), essential for IQ modulation. Our VπL is ∼5 times lower than the lowest thin-film LN MZMs and ∼3 times lower than the lowest Si MZMs. This enables devices with complementary metal-oxide semiconductor compatible VπL (<1Vcm) and smaller footprint than LN or Si MZMs, improving circuit density and reducing power consumption by 1 order of magnitude.

Smart forest monitoring: A novel Internet of Things framework with shortest path routing for sustainable environmental management

AbstractForests play a pivotal role in protecting the environment, preserving vital natural resources, and ultimately sustaining human life. However, the escalating occurrences of forest fires, whether of human origin or due to climate change, poses a significant threat to this ecosystem. In recent decades, the emergence of the IoT has been characterised by the utilisation of smart sensors for real‐time data collection. IoT facilitates proactive decision‐making for forest monitoring, control, and protection through advanced data analysis techniques, including AI algorithms. This research study presents a comprehensive approach to deploying a dynamic and adaptable network topology in forest environments, aimed at optimising data transmission and enhancing system reliability. Three distinct topologies are proposed in this research study: direct transmission from nodes to gateways, cluster formation with multi‐step data transmission, and clustering with data relayed by cluster heads. A key innovation is the use of high‐powered telecommunication modules in cluster heads, enabling long‐range data transmission while considering energy efficiency through solar power. To enhance system reliability, this study incorporates a reserve routing mechanism to mitigate the impact of node or cluster head failures. Additionally, the placement of gateway nodes is optimised using meta‐heuristic algorithms, including particle swarm optimisation (PSO), harmony search algorithm (HSA), and ant colony optimisation for continuous domains (ACOR), with ACOR emerging as the most effective. The primary objective of this article is to reduce power consumption, alleviate network traffic, and decrease nodes' interdependence, while also considering reliability coefficients and error tolerance as additional considerations. As shown in the results, the proposed methods effectively reduce network traffic, optimise routing, and ensure robust performance across various environmental conditions, highlighting the importance of these tailored topologies in enhancing energy efficiency, data accuracy, and network reliability in forest monitoring applications.

High color rendering index WLEDs enabled by multi-band tuning of InP quantum dots

Quantum dots (QDs) represent a significant class of fluorescent materials, offering the potential to reduce power consumption and enhance the color rendering index (CRI) of white light-emitting diode (WLED) devices. However, the presence of toxic elements such as Cd and Pb in traditional fluorescent QDs limits their widespread commercial application. Compared to the broad emission characteristics of environmentally friendly AgInS2 and CuInS2 QDs, the narrower linewidth of InP-based core-shell QDs is advantageous for developing WLED devices with a higher CRI. In this study, we employed a multistage heating method to synthesize a series of amino-phosphine-based InP/ZnSe core-shell QDs, which exhibited emission wavelengths ranging from 535 to 650 nm, narrow emission linewidths of 43-47 nm, and high photoluminescence quantum yields of 60%-80%. Subsequently, six different color QDs are used to fabricate a WLED device. The best WLEDs show not only bright warm light (correlated color temperature = 3323 K) with a maximum luminous efficacy of 74.1 lm W-1, but also excellent color quality (CRI Ra = 93, as well as R9 = 94.8, and R13 = 97.1). These results indicate remarkable progress in InP-based WLEDs for high-quality lighting applications.

Piezo-VFETs: Vacuum Field Emission Transistors Controlled by Piezoelectric MEMS Sensors as an Artificial Mechanoreceptor with High Sensitivity and Low Power Consumption.

As one of the most promising electronic devices in the post-Moore era, nanoscale vacuum field emission transistors (VFETs) have garnered significant attention due to their unique electron transport mechanism featuring ballistic transport within vacuum channels. Existing research on these nanoscale vacuum channel devices has primarily focused on structural design for logic circuits. Studies exploring their application potential in other vital fields, such as sensors based on VFET, are more limited. In this study, for the first time, the design of a vacuum field emission transistor (VFET) coupled with a piezoelectric microelectromechanical (MEMS) sensing unit is proposed as the artificial mechanoreceptor for sensing purposes. With a negative threshold voltage similar to an N-channel depletion-mode metal oxide silicon field effect transistor, the proposed VFET has its continuous current tuned by the piezoelectric potential generated by the sensing unit, amplifying the magnitude of signals resulting from electromechanical coupling. Simulations have been conducted to validate the feasibility of such a configuration. As indictable from the simulation results, the proposed piezoelectric VFET exhibits high sensitivity and an electrically adjustable measurement range. Compared to the traditional combination of piezoelectric MEMS sensors and solid-state field effect transistors (FETs), the piezoelectric VFET design has a significantly reduced power consumption thanks to its continuous current that is orders of magnitude smaller. These findings reveal the immense potential of piezoelectric VFET in sensing applications, building up the basis for using VFETs for simple, effective, and low-power pre-amplification of piezoelectric MEMS sensors and broadening the application scope of VFET in general.

Percutaneous Wearable Biosensors: A Brief History and Systems Perspective

AbstractWearable biosensors are envisioned to disrupt both delivery and accessibility of healthcare by providing real‐time, continuous monitoring of informative and predictive physiological markers in convenient, user‐friendly, and portable designs. In recent years, there has been myriad demonstrations of biosensor‐integrated clothing and skin‐borne biosensor patches, enabled by device miniaturization, reduced power consumption, and new biosensing chemistries. Despite these impressive demonstrations, most consumer‐grade wearables have been limited to biophotonic and biopotential sensing methods to extrapolate information such as pulse, blood oxygenation, and electrocardiograms. The only commercial example of wearable electrochemical sensing methods is for glucose monitoring. However, there is a growing interest in developing percutaneous biosensors for monitoring in interstitial fluid (ISF), which offers direct access to popular analytes such as glucose, lactate, and urea, as well as new targets like hormones, antibodies, and even medications. Herein, a brief context for the current status of wearable biosensors is provided and assess the major engineering successes and pitfalls of percutaneous biosensors over the past five years, with a view to identifying areas for further developments that will enable deployable, clinical‐ or consumer‐grade systems.

Spike-based rotation detection using a self-powered triboelectric sensor with mimicry of a vestibular system

The biological vestibular system in semicircular canals detects head rotation with high energy efficiency through its unique spike-based processing. We propose a self-powered vestibular sensory neuron inspired by the biological vestibular system and utilizing a triboelectric nanogenerator (TENG) as a rotation sensor and a neuron transistor (neuristor) as a spiking neuron. This artificial vestibular neuron simultaneously detects the angular velocity and encodes it into spikes. When the angular velocity that is applied to the TENG increases, the spiking frequency increases, and this is a desired property of the vestibular neuron. Therefore, the vestibular neuron can serve as an input neuron in a spiking neural network (SNN). We also demonstrate a fully hardware-based neuromorphic system that is attractive for its improved energy efficiency to recognize rotation axes. Compared to a vestibular system based on conventional von-Neumann computing, that based on the proposed neuromorphic computing can effectively reduce power consumption by adopting spike transmission and employing the self-powered sensor. The proposed system is promising for applications related to rotation that demands high energy-efficiency.

An Efficient RI-MP2 Algorithm for Distributed Many-GPU Architectures.

Second-order Møller-Plesset perturbation theory (MP2) using the Resolution of the Identity approximation (RI-MP2) is a widely used method for computing molecular energies beyond the Hartree-Fock mean-field approximation. However, its high computational cost and lack of efficient algorithms for modern supercomputing architectures limit its applicability to large molecules. In this paper, we present the first distributed-memory many-GPU RI-MP2 algorithm explicitly designed to utilize hundreds of GPU accelerators for every step of the computation. Our novel algorithm achieves near-peak performance on GPU-based supercomputers through the development of a distributed memory algorithm for forming RI-MP2 intermediate tensors with zero internode communication, except for a single asynchronous broadcast, and a distributed memory algorithm for the energy reduction step, capable of sustaining near-peak performance on clusters with several hundred GPUs. Comparative analysis shows our implementation outperforms state-of-the-art quantum chemistry software by over 3.5 times in speed while achieving an 8-fold reduction in computational power consumption. Benchmarking on the Perlmutter supercomputer, our algorithm achieves 11.8 PFLOP/s (83% of peak performance) performing and the RI-MP2 energy calculation on a 314-water cluster with 7850 primary and 30,144 auxiliary basis functions in 4 min on 180 nodes and 720 A100 GPUs. This performance represents a substantial improvement over traditional CPU-based methods, demonstrating significant time-to-solution and power consumption benefits of leveraging modern GPU-accelerated computing environments for quantum chemistry calculations.

Molecular Reconfiguration of Disordered Tellurium Oxide Transistors with Biomimetic Spectral Selectivity.

Reconfigurable devices with field-effect transistor features and neuromorphic behaviors are promising for enhancing data processing capability and reducing power consumption in next-generation semiconductor platforms. However, commonly used 2D materials for reconfigurable devices require additional modulation terminals and suffer from complex and stringent operating rules to obtain specific functionalities. Here, a p-type disordered tellurium oxide is introduced that realizes dual-mode reconfigurability as a logic transistor and a neuromorphic device. Due to the disordered film surface, the enhanced adsorption of oxygen molecules and laser-induced desorption concurrently regulate the carrier concentration in the channel. The device exhibits high-performance p-type characteristics with a field-effect hole mobility of 10.02 cm2 V-1 s-1 and an Ion/Ioff ratio exceeding 106 in the transistor mode. As a neuromorphic device, the vision system exhibits biomimetic bee vision, explicitly responding to the blue-to-ultraviolet light. Finally, in-sensor denoising and invisible image recognition in static and dynamic scenarios are achieved.

An optimized decentralized peer-to-peer energy trading system for smart grids incorporating uncertain renewable energy sources through fuzzy optimization

The significant issue in today's world is that the power system is undergoing on account of unafforded power generation and distribution. Consequently, improper utilization of renewable energy, as well as its pricing mechanism of electrical energy, is also a vital issue in transmission and distribution systems (TDS). In addition, in the context of upcoming power systems, ensuring energy stability and effective energy management have become paramount. This paper introduces a novel approach to address these challenges by proposing an optimal demand response grid-connected decentralized peer-to-peer (P2P) energy trading system. This system efficiently manages energy transactions among prosumers, the grid, and renewable energy sources (RES) under uncertain conditions. Fuzzy optimization (FO) is employed to facilitate interconnections between these entities. A fuzzy logic controller with intelligent rules governs the overall strategy and pricing mechanism based on prosumer power deviation (PPD). Inspired by human thinking, these rules prioritize P2P transactions, with surplus energy exported to the main grid (MG) and energy imported from the MG when necessary. Pricing adjusts dynamically with load demand. Simulation results demonstrate the effectiveness of the fuzzy rules across various conditions, ensuring proper system operation. The proposed model also proves that the P2P energy trading system has reduced power consumption by balancing the generated power and the load demand. The proposed system is environmentally friendly, emitting no harmful gases.

Design of Low Power Control Unit for RISC-V Processor Core

This research work focuses on the development of a low-power decode logic for a RISC-V processor core with specifications. The goal is to create a controller that performs all six groups of instruction formats outlined in the RV32I Base Integer Instruction Set. The control unit is designed to decode a total of 13 instruction sets, allowing for a comprehensive range of operations. A single instruction pipeline approach is implemented in the design to optimize performance. The synthesis of the design is carried out using the 32 nm standard library, resulting in a maximum operating frequency of 666.67 MHz. To further enhance power efficiency, clock gating techniques are employed, leading to a reduction in power consumption by 18.72 % from 112.15 µW to 91.45 µW. Additionally, the layout of the design is optimized, resulting in an area of 354.74 mm2. The successful development of this low-power decode logic demonstrates its potential for integration into larger RISC-V processor cores. Future enhancements can include expanding the instruction decoding capability to encompass the full range of 47 instructions in the RV32I Base Integer Instruction Set, as well as exploring additional pipeline stages to further improve performance. The results achieved in this project contribute to the ongoing pursuit of power-efficient and high-performance processor designs based on the RISC-V architecture.

Ultra‐Low BER Encrypted Communication Based on Self‐Powered Bipolar Photoresponse Ultraviolet Photodetector

AbstractUnderwater visible light communication often faces risks such as susceptibility to external light source interference, signal distortion, and information leakage. The cuprous oxide modified gallium oxide nanorod arrays detector prepared in this study can generate bipolar photocurrents under dual‐wavelength ultraviolet light without external bias voltage. Based on it, the constructed self‐powered ultraviolet ASCII code communication system not only reduce power consumption but also significantly enhance anti‐interference capability. Furthermore, the developed solar‐blind ultraviolet AMI and HDB3 ternary code communication systems can realize non‐visible light secure communication and automatic error correction. Notably, the encrypted communication system assembled using the additive property of bipolar photocurrents can also significantly improve communication security and reliability, whose bit error rate is only 0.26‰. This achievement contributes to technical advancements in underwater channel encryption and provides valuable insights for the further application of encrypted communication coding techniques.

Experimental and numerical study on scroll compressor under different profile correction and discharge ports shapes

The scroll compressor is an essential component in air conditioning and heat pump systems. This study examined an electric scroll compressor using R22 refrigerant, focusing on how modifications in the scroll head profile and discharge structure affect its performance. Initially, a semi-empirical model based on mathematical formulas was developed to predict the performance improvements of the updated scroll compressor. Three-dimensional simulation software was then used to illustrate changes in the internal flow dynamics before and after enhancements. The predictions were validated through experimental testing. Both the semi-empirical model and the three-dimensional unsteady numerical calculation model analyzed the effects of modifications to the tooth head profile and discharge port shape on compressor performance. The adoption of a PMP profile correction delayed the compressor discharge process, shifting the starting discharge angle from 315°to 344°, thereby reducing under-compression occurrences during operation and increasing volumetric efficiency by 1.31%. Additionally, mathematical formulas correlating temperature with compressor performance were developed from fitting data, predicting compressor behavior under various conditions. Moreover, replacing a circular discharge port with a waist-shaped port reduced power consumption by 0.59 kW, resulting in a more uniform temperature distribution and enhanced mass flow rate during compressor operation.

A certain examination on heterogeneous systolic array (HSA) design for deep learning accelerations with low power computations

Acceleration techniques play a crucial role in enhancing the performance of modern high-speed computations, especially in Deep Learning (DL) applications where the speed is of utmost importance. One essential component in this context is the Systolic Array (SA), which effectively handles computational tasks and data processing in a rhythmic manner. Google's Tensor Processing Unit (TPU) leverages the power of SA for neural networks. The core SA's functionality and performance lies in the Computation Element (CE), which facilitates parallel data flow. In our article, we introduce a novel approach called Proposed Systolic Array (PSA), which is implemented on the CE and further enhanced with a modified Hybrid Kogge Stone adder (MHA). This design incorporates principles to expedite computations by rounding and extracting data model in SA as PSA-MHA. The PSA, utilizing a data flow model with MHA, significantly accelerates data shifts and control passes in execution cycles. We validated our approach through simulations on the Cadence Virtuoso platform using 65 nm process technology, comparing it to the General Matrix Multiplication (GMMN) benchmark. The results showed remarkable improvements in the CE, with a 30.29 % reduction in delay, a 23.07 % reduction in area, and an 11.87 % reduction in power consumption. The PSA outperformed these improvements, achieving a 46.38 % reduction in delay, a 7.58 % reduction in area, and an impressive 48.23 % decrease in Area Delay Product (ADP). To further substantiate our findings, we applied the PSA-based approach to pre-trained hybrid Convolutional and Recurrent (CNN-RNN) neural models. The PSA-based hybrid model incorporates 189 million Multiply-Accumulate (MAC) units, resulting in a weighted mean architecture value of 784.80 for the RNN component. We also explored variations in bit width, which led to delay reductions ranging from 20.17 % to 30.29 %, area variations between 13.08 % and 32.16 %, and power consumption changes spanning from 11.88 % to 20.42 %.

A FinFET Based Low-Power Write Enhanced SRAM Cell With Improved Stability

This work introduces a FinFet based low-power 11T SRAM cell with write enhanced feature, which is considered to meet modern technology requirements due to its distinctive features and performance. The proposed 11T SRAM cell is designed in such a way, so that it reduces the overall power consumption, improving access time, and static noise margin (SNM), especially during the write operation. The proposed 11T SRAM cell is compared with other SRAM cells, including conventional 6T (conv6T), dual pull-up transistor 10T (DPUT10T), dual pull-down transistor 10T (DPDT10T), dual transmission gate 9T (DTG9T), and dual transmission gate 10T (DTG10T), at an equitable standard. The results obtained at the supply voltage of 0.5 V @ 27 °C shows the reduction in leakage power during hold 1 operation by 1.61×/1.33×/1.44×/1.63×/1.46×, and reduction in power consumption during read operation by 1.02×/1.55×/ 1.01×/1.81×/1.97× in comparison with conv6T/DPUT10T/ DPDT10T/DTG9T/DTG10T, respectively. Write access time is also improved by a factor of 1.67×/1.71×/2.59×/1.45×/1.97×, respectively. Additionally, read static noise margin (RSNM) and write static noise margin (WSNM) are improved by 2.03×/1.01×/1.65×/1.54×/1.43×, and 1.42×/1.33×/1.41×/1.02×/1.62×, respectively. This demonstrates why the proposed low-power 11T SRAM cell is desirable, especially when compared to the other cells under consideration.