Area Consumption Research Articles

Theoretical review based on the technologies developed in solar desalination.

Increasing global demand for water and energy has given rise to renewable energies that offer an environmentally friendly alternative. Solar desalination systems have become a very attractive topic, due to the fact that areas lacking fresh water have a wealth of solar energy, allowing to present a convenient, promising and viable solution, also obtaining drinking water for consumption in remote areas from water with high salinity, where it is considered to be a process free of CO2 emissions when powered by solar radiation, being sustainable and environmentally friendly. The main objective of this study is to provide an extensive review of the different solar desalination systems, evaluating the indicators, factors and technologies involved in the process, through scientific sources such as: articles, academic publications, international congresses, indexed journals, and others.

Mitigating the Energy Consumption and Carbon Emissions of a Residential Area in a Tropical City Using Digital Twin Technology: A Case Study of Bertam, Penang

As of 2022, roughly 79.8% of Malaysia’s population resides in urban areas, increasing the population density of its cities. The hot and humid climate in Malaysia necessitates the constant use of air conditioning, especially in cities, resulting in high residential electric consumption and carbon emissions. The residential sector significantly contributes to global climate change, accounting for 27% of global energy consumption and 17% of carbon emissions. To address this concern, the local framework of the National Low Carbon Cities Masterplan (NLCCM) is advocating for a minimum 33% reduction in carbon emissions by 2030 in urban settings, aligning with the commitments made at the 15th Conference of Parties (COP 15). The aim of this study was to determine the energy consumption and carbon emission of residential areas in a tropical city, as well as explore potential energy and carbon savings. Utilizing the Intelligent Communities Lifecycle–Intelligent Community Design (iCL-iCD) energy simulation software, a digital twin of Bertam City, Penang, was developed. This digital model included 65.4% residential housing, which, in the specific scenario under study, accounted for 36% of the city’s electrical energy consumption for cooling purposes only. An early simulation of the residential areas of the city estimated the baseline energy consumption and carbon emissions to be 607 GWh and 314,736 tCO2e, respectively. Several energy-efficient measures were applied to the residential area of Bertam City, revealing a potential saving of 37.3% in both energy and carbon emissions.

Modified Broadband Ruthroff-Type Transmission Line Transformer Balun for Isolation-Enhanced Passive Mixer Design.

Generalized broadband operation facilitates multifunction or multiband highly integrated applications, such as modern transceiver systems, where ultra-wideband bidirectional passive mixers are favored to avoid a complex up/down-conversion scheme. In this paper, a modified Ruthroff-type transmission line transformer (TLT) balun is presented to enhance the isolation of the mixer from the local oscillator (LO) to the radio frequency (RF). Compared to the conventional methods, the proposed Ruthroff-type architecture adopts a combination of shunt capacitors and parallel coupled lines to improve the return loss at the LO port, thus effectively avoiding the area consumption for the diode-to-balun impedance transformation while simultaneously providing a suitable point for IF extraction. In addition, a parallel compensation technique consisting of an inductor and resistor is applied to the RF balun to significantly improve the amplitude/phase balance performance over a wide bandwidth. Benefiting from the aforementioned operations, an isolation-enhanced 8-30 GHz passive double-balanced mixer is designed as a proof-of-principle demonstration via 0.15-micrometer GaAs p-HEMT technology. It exhibits ultra-broadband performance with 7 dB average conversion loss and 50 dB LO-to-RF isolation under 15 dBm LO power. The monolithic microwave integrated circuit area is 0.96 × 1.68 mm2 including all pads.

Security Enabled New Term Weight Measure Technique with Data Driven for Next Generation Mobile Computing Networks

In the field of ASIC and FPGA, Machine Learning (ML) techniques play a major role and become predominant for accurate results for different applications like big data analysis and automotive electronics, and driverless vehicles which are required speed and power savings. Due to increasing the demand for higher accuracy, low power, low area consumption, and higher throughput for the complexity of the designs in the latest technology, the proposed system is fulfilling these demands in ASIC and FPGA domains, reconfigurable hardware architecture has been proposed it consists of an ML-based Support Vector Machine (SVM), high-speed AHB protocol and Floating point (FP) operations and also the system has the flexibility to communicate with I2C and I2S protocols. In order to increase throughput with minimal latency, the proposed architecture with AHB protocol and AHB to APB bridge is incorporated between the fabric dynamically reconfigurable multi-processor (FDPM) and peripherals along with security algorithms using SHA-256bits and AES. In order to perform ML-based applications, the proposed system is incorporated double-precision floating point (DPFP) arithmetic operations. The overall proposed architecture is developed in Verilog HDL and quality checking using the LINT tool and Clock Domain Crossing (CDC) using Spyglass tool and synthesized using DC compiler for ASIC and Vivado Design Suite 2018.1 for FPGA implementation and verification. The entire design is interfaced with the Zynq processor and SDK tool to verify data transfer between hardware and software. The obtained results show the generated custom accelerator is able to compute any complex ML classifiers for a larger amount of data. The obtained results are compared with existing state-of-art results and found that 18 % improvement in throughput, a 21 % improvement in power consumption savings, and a 34 % reduction in latency.

Traffic light optimization with low penetration rate vehicle trajectory data

Traffic light optimization is known to be a cost-effective method for reducing congestion and energy consumption in urban areas without changing physical road infrastructure. However, due to the high installation and maintenance costs of vehicle detectors, most intersections are controlled by fixed-time traffic signals that are not regularly optimized. To alleviate traffic congestion at intersections, we present a large-scale traffic signal re-timing system that uses a small percentage of vehicle trajectories as the only input without reliance on any detectors. We develop the probabilistic time-space diagram, which establishes the connection between a stochastic point-queue model and vehicle trajectories under the proposed Newellian coordinates. This model enables us to reconstruct the recurrent spatial-temporal traffic state by aggregating sufficient historical data. Optimization algorithms are then developed to update traffic signal parameters for intersections with optimality gaps. A real-world citywide test of the system was conducted in Birmingham, Michigan, and demonstrated that it decreased the delay and number of stops at signalized intersections by up to 20% and 30%, respectively. This system provides a scalable, sustainable, and efficient solution to traffic light optimization and can potentially be applied to every fixed-time signalized intersection in the world.

Does non-farm employment increase rural households’ consumption in western Ethiopia? Empirical evidence from the horo guduru wollega zone

In rural areas where farming is the primary source of income for farmers, engaging in non-farm activities can provide an additional source of income and improve household consumption. However, several social and economic factors present challenges to the involvement of rural households in non-farm activities. This study aimed to examine the impact of non-farm participation on household consumption in rural areas.A multistage sampling method was used to select the sample households from the study area. Heckman's two-step procedure was utilized to analyze survey data obtained from 383 rural household heads. The results indicated that participating in non-farm employment had a positive and significant effect on household consumption levels. The result indicated that households that engage in non-farm activities had higher levels of consumption compared to those who do not participate in such activities. The study also identified several factors that influence household consumption in rural areas. These factors include education, access to credit, distance to market, TLU (Tropical Livestock Unit), membership in “iqub" (a social financial system), health status, non-farm training, and gender. It was found that gender disparities exist, with female-headed households experiencing lower consumption levels than male-headed households. Based on the findings, the research recommended addressing gender disparities and improving rural infrastructure, particularly in relation to enhancing health services, electricity supply, road transport, and education. Efforts should also be made to overcome the challenges related to access to training and credit. The study emphasized the significance of recognizing the impact of supporting existing social financial systems, such as iqub, in improving household consumption in rural areas.

Advancing Hardware Security: A Review and Novel Design of Configurable Arbiter PUF with DCM-Induced Metastability for Enhanced Resource Efficiency and Unpredictability

As the Internet of Things (IoT) and Blockchain technologies continue to assert their dominance in the technical landscape, the demand to enhance security measures becomes foremost. In this context, Physical Unclonable Functions (PUFs) are widely used hardware security primitives that can be used to solve a wide range of security issues. To support hardware security solutions, this paper presents an extensive overview and analysis of the existing Physical Unclonable Functions (PUFs) used as True Random Number Generators (TRNGs). Recognizing the shortcomings of current PUF designs, we propose a configurable Arbiter PUF design employing Digital Clock Manager (DCM)-induced metastability as an entropy source, presenting a robust solution for evolving hardware security. To mitigate the adverse consequences of metastability, the proposed Arbiter PUF includes a Carry Chain primitive with four Flip-Flop clones. Acknowledging the constantly evolving IoT and Blockchain environment, the suggested configurable Arbiter PUF is made to satisfy the highest security standards. By exploiting the inherent variations in FPGA technology, we aim to reduce system resource and area consumption, aligning with the efficiency criteria of modern applications. The system's performance is additionally enhanced by an on-chip post-processing based on DSP. Simulation results demonstrate successful implementation on a Xilinx Basys-3 FPGA board, offering a scalable and efficient solution. The generated sequences of the proposed PUF undergo rigorous testing, including National Institute of Standards and Technology (NIST) statistical tests for uniqueness, reliability, and randomness. This holistic approach aims to improve the PUF's performance and security.

From drainage to resource: a practice approach to reuse greywater for household irrigation purposes

Abstract The United Nations indicates that available freshwater resources will decrease significantly due to pollution growth and urbanization; two-thirds of the world's population may face water shortages by 2030. Extended use of greywater is an alternative option for reducing potable water consumption in urban areas. Recently, the reuse of treated greywater for home gardens, peri-urban agriculture, and landscaping has become a widespread concern in many developing countries. This paper presents a study on a low-cost system that can perform greywater treatment for household use. This treatment system employed physical filtration by ceramic filters, quartz gravel, hollow fiber membrane, and UV disinfection. Three greywater samples collected from the kitchen, washing basins, and bathroom were investigated. The operation process determines the system's effectiveness by considering turbidity, coliform, Biochemical Oxygen Demand (BOD5), and Chemical Oxygen Demand (COD) concentration of the inlet and outlet water. As a result, high removal efficiency (i.e., >60%) could be obtained for each investigated parameter. Results also showed that grey water generated from washing basins has the highest potential for reuse since the water quality after treatment satisfies the water reuse standards for household irrigation. The findings encourage further exploration and implementation of greywater reuse practices.

A 48-Channel High-Resolution Ultrasound Beamforming System for Ultrasound Endoscopy Applications

We introduce a highly efficient 48-channel ultrasound beamforming system ideal for ultrasound endoscopy applications. The system includes a transmitter and a receiver that allows for low-area, high-resolution imaging acquisition. The transmitter uses a charge redistribution HV (high-voltage) scheme to generate three-level pulses that actuate the transducer, implemented with the standard CMOS process for optimal cost and power savings. Meanwhile, the receiver features a sub-array structure and a delay generator that reduces the area usage. To achieve high-resolution ultrasound imaging acquisition with low computational power, we developed a Shift Coherence Factor (SCF) algorithm that is hardware-friendly. This approach delivers a lateral resolution of over 20% better than that of the conventional delay and sum (DAS) algorithm, with a contrast ratio of over 30 dB. The system was implemented in a 180 nm standard CMOS process with an area of 24.98 mm2, power consumption of 8.23 mW per channel, achieving a delay resolution of 8.33 ns, and a low-area implementation of 0.52 mm2 per channel. The system offers high-quality imaging acquisition with minimal additional area and power consumption, which has great potential for 3D imaging or catheterized ultrasound systems.

Comparative analysis of 2D mesh topologies with additional communication links for on-chip networks

Multiprocessor system-on-chip (MPSoC) is playing a vital role in recent embedded technologies. One of the main challenges of this system is its communication bottleneck which limits performance scaling capabilities. In order to overcome this restriction, on-chip interconnection networks have been proposed. Several topologies have been suggested in the literature, but the most popular one remains the mesh topology due to its systematic and scalable design structure. Although, the functionality and performance of network-based mesh topologies degrade significantly with growing system size, which results in the network diameter and the dependency on small bisection width. A wide range of modified designs has been proposed in the literature with various types of additional links to improve the network performance. However, these links result in additional cost overhead on the system in terms of area, energy, and power consumption. Among the selected competitor meshes, it is observed that the link design of d-Torus produces high performance at elevated communication costs. The design of the CBP-Torus is less complex and results in lower communication costs compared to d-Torus with similar performance. The selection of an appropriate mesh-type topology of the target application is, therefore, a tradeoff between the performance and cost of the network. This paper analyzes the impact of additional links on the performance of on-chip mesh architectures. The comparative analysis includes performance characteristics, structural complexity, power consumption, and area utilization. This research work provides a roadmap for the designers to choose the most appropriate topology for a particular design.

A dependable and resource-frugal ring oscillator physically unclonable function

ABSTRACT Silicon Physical Unclonable Function (PUF) is a crucial component for hardware security, providing unique device identification in networks. PUF provides robust security for cryptographic keys and IC digital fingerprints. However, resource constraints in network devices limit its use in security protocols. To address this challenge, PUF offers a lightweight hardware security solution with two main types: strong PUF (e.g. arbiter PUF) and Ring Oscillator (RO) PUF. The first option produces large CRPs with improved security but incurs hardware overhead, whereas the second option offers lower overhead with smaller CRP size, leading to reduced security. This paper introduces a novel, cost-effective solution to enhance RO PUF security while reducing hardware limitations. Our proposal involves a resource sharing technique with scan flip-flops, multiplexers, and inverter logic. This approach takes advantage of existing scan chains in circuit netlists to reduce PUF area without sacrificing uniqueness. A specific set of test patterns and odd number of inverters transform the memory-based PUF into an RO PUF. Our solution also leverages pass transistor-based implementation to significantly reduce power consumption and area overhead. Simulation results show that our approach effectively reduces area and power consumption while maintaining PUF uniqueness.

Social Currency: Analysis of The Economic Potential of Community Development Banks as Instruments of Public Policies in Brazil

Objective: Analyze Community Development Banks and social currencies as instruments of public policies. Theoretical Framework: The study includes theoretical approaches on Community Development Banks and public policies focused on solidarity economy and social currency. Method: The study is a basic, qualitative research grounded in a descriptive approach method, supported by a literature review. Results and conclusion: It was observed that Community Development Banks adopt solidarity financial practices and financial inclusion by facilitating access to credit for individuals in socially vulnerable situations. Social currencies emerge as instruments aiming to energize the local economy through Community Development Banks, promoting the inclusion of marginalized populations. However, there is still much to be explored regarding complementary social currencies, and many municipalities are replicating and implementing this model in their territories to leverage local development. Simultaneously, resources are made available to enhance consumption and income in areas with low levels of credit conditions and financial literacy. Implications of the research: The study emphasizes the need to address Community Development Banks and, particularly, the establishment of public policies for solidarity economy and the creation of social currencies, considering the diversity of each locality. Originality/value: This study offers an original and valuable approach in analyzing Community Development Banks and social currencies as instruments of public policy. It reveals significant insights into the effectiveness of these banks in promoting solidarity financial practices and financial inclusion, especially for those in socially vulnerable situations.

Investigating the Effect of Living Walls on Cooling Energy Consumption in Various Urban Microclimates, Building Heights, and Greenery Coverage Areas

With the aim of addressing the significant contribution of buildings to global energy consumption and the growing need to establish a more harmonious connection between humans and nature in urban environments, the concept of green vertical systems has garnered considerable attention. These systems possess the potential to enhance the energy efficiency and environmental sustainability of buildings. This study seeks to explore the impact of living walls on the energy performance of buildings, specifically investigating the influence of a living-walled urban block on the ambient air temperature and its subsequent effect on building energy consumption. By comparing the cooling energy consumption of two typical buildings situated in Shiraz, southern Iran, with varying levels of greenery in different microclimates, we further ascertain the effectiveness of living walls in reducing energy consumption. To conduct this analysis, we employed coupled simulations utilizing EnergyPlus and Envi-met, which incorporated both the outdoor microclimate and the cooling energy consumption of the buildings. The urban block under consideration consisted of 48 three-story buildings (Case A) and five-story buildings (Case B) within a site measuring 120 m × 150 m. Our findings substantiate that the implementation of a living wall system can lower the ambient air temperature by a significant extent, with an average decrease of 1.35 °C and a maximum decrease of 2.25 °C. Consequently, living walls can effectively mitigate the urban heat island phenomenon by decreasing the temperatures of the surrounding buildings. Additionally, our investigations revealed a maximum energy saving of 15%, with microclimate exerting a 4.3% influence on these savings.

WiSOM: WiFi-enabled self-adaptive system for monitoring the occupancy in smart buildings

There has been extensive research on building energy saving (BES), which aims to reduce energy consumption inside buildings. One of the key solutions for energy saving in buildings is to reduce energy consumption in areas that are not occupied by inhabitants. However, effective monitoring of occupants for energy-saving purposes can be challenging due to unpredictable variations in the indoor environment, such as variations in space size, furniture arrangement, the nature of occupants’ activities (e.g., varied intensities and instances), and penetration losses of walls. Unfortunately, the existing solutions for occupancy monitoring in smart buildings, such as PIR sensors, CO2 sensors, and cameras, etc., are expensive, require excessive maintenance, and are not adaptable to the complex variations in indoor environments. This paper introduces WiSOM, for occupancy detection that utilizes the channel state information (CSI), of commodity WiFi. The method is self-adaptive and designed to handle complex variations in indoor environments. We conducted a thorough analysis of WiSOM and evaluated it under various indoor conditions, including the impact of multipath effects, the detection of different intensities and instances of activities of daily living (ADL), and the impact of wall absorption in a real-home scenario. Our evaluation demonstrated an average detection rate of 98.25% for multipath effects, 96.5% and 98.1% for different intensities and instances of ADL, and 94.4% for wall absorption. Additionally, we assessed WiSOM’s resilience to temporal variation in the CSI and achieved a false alarm rate of less than 2%. In comparison to recent baselines, WiSOM outperformed, achieving up to a 21% improvement in detection rate within real-house scenarios.

SNNSim: Investigation and Optimization of Large‐Scale Analog Spiking Neural Networks Based on Flash Memory Devices

Spiking neural networks (SNNs) have emerged as a novel approach for reducing computational costs by mimicking the biologically plausible operations of neurons and synapses. In this article, large‐scale analog SNNs are investigated and optimized at the hardware‐level by using SNNSim, the novel simulator for SNNs that employ analog synaptic devices and integrate‐and‐fire (I&F) neuron circuits. SNNSim is a reconfigurable simulator that accurately and very quickly models the behavior of the user‐defined device characteristics and returns key metrics such as area, accuracy, latency, and power consumption as output. Notably, SNNSim exhibits exceptional efficiency, as it can process the entire 10 000 Modified National Institute of Standards and Technology (MNIST) test dataset in a few seconds, whereas SPICE simulations require hours to simulate a single MNIST test data. Using SNNSim, the conversion of artificial neural networks (ANNs) to SNNs is simulated and the performance of the large‐scale analog SNNs is optimized. The results enable the design of accurate, high‐speed, and low‐power operation of large‐scale SNNs. SNNSim code is now available at https://github.com/SMDLGITHUB/SNNSim.

Experimental and numerical investigation on the charging and discharging process of a cold energy storage for space cooling of buildings

To address the current issue of the difficulty to use the hot outdoor environment during summer nights to provide coolness for phase change materials (PCM) and thus reduce building energy consumption in hot summer areas, a novel cold energy storage unit has been proposed. It replaces the hot outdoor environment with other free or cheap cold sources. An experimental setup is built to investigate the cold storage and release performance of the PCM storage unit. Later, the well-validated numerical model is conducted to further explore thermal performance of the PCM storage unit. The numerical result indicates that the cold storage process can be completed from 64.33 min to 96.56 min when the water inlet temperature varies from 10 °C to 16 °C. Further, during the cold release process, the PCM storage unit can reduce the temperature of the hot air by a maximum of about 8–12 °C when the air inlet temperature ranges from 32 °C to 38 °C. The cold release efficiency is varied between 81.65 % and 96.44 %. In conclusion, the findings provide a promising way for cold storage and space cooling of buildings in hot summer areas through the cold energy storage unit.

Edge-sorter: A hardware sorting engine for area & power constrained edge computing devices

In recent years, hardware sorters have been an attracted topic for researchers. Since hardware sorters play a crucial role in embedded systems, several attempts have been made to efficiently design and implement these sorters. Previous state-of-the-art hardware sorters are not suitable for embedded edge computing devices because they (1) consume high power, (2) occupy high area, (3) work for limited data-width numbers, (4) require many memory resources, and (5) finally, their architecture is not scalable with the number of input records. This paper proposes a hardware sorter for edge devices with limited hardware resources. The proposed hardware sorter, called Edge-Sorter, processes 4 bits of input records at each clock cycle. Edge-Sorter utilizes the unary processing in its main processing core. Edge-Sorter has valuable attributes compared to previous state-of-the-art techniques, including low power consumption, low area occupation, sorting numbers without storing their indices, sorting numbers with arbitrary data-width, and scalable with the number of input records. The proposed approach is evaluated and compared with previous state-of-the-art techniques with two different implementation and synthesis environments: Xilinx Vivado FPGA-based and Synopsys Design Compiler 45-nm ASIC-based. The Synthesis results of both environments indicate that both Edge-Sorter techniques reduces area and power consumption on average by 80% and 90%, respectively compared to previous techniques.

Community Antibiotic Consumption in Cyprus for the Period 2015 to 2022.

Cyprus currently reports to ESAC-Net the total consumption of antimicrobials, without distinguishing between hospital and community-based antibiotic use. As a result, these data can only provide generalized insights into antimicrobial trends in the country. This study is a first attempt to retrospectively analyze community antibiotic consumption in Cyprus for the period of 2015 to 2022. Data on community antimicrobial consumption between 2015 and 2022 were extracted from Pharmatrack's database. Orally administered dispensed antibiotics were categorized under the J01 group of the WHO Anatomical Therapeutic Chemical (ATC) classification and by the WHO's AWaRe classification of antibiotics. Antibiotic consumption was calculated in both packages consumed and per 1000 inhabitants, overall, by year of consumption and districts. During the period of 2015-2022, there was variability in the mean outpatient antibiotic consumption per 1000 inhabitants among the five districts in Cyprus. Community consumption increased by 38% throughout the study period. Additionally, a decrease of 3% in the consumption of WHO 'Access' antibiotics was observed, accompanied with a concurrent increase of 3% in the 'Watch' group. Specifically, in 2022 the WHO 'Access' group consumption in the Cypriot community was 48%, significantly lower than the WHO's goal of 60% and the EU's goal of 70% for 'Access' antibiotic consumption. Antibiotic consumption in the community of Cyprus between 2015 and 2022 demonstrated substantial variability among districts, with higher consumption in less populated areas. There was an increasing trend in community consumption over the years and a decreasing trend in the percentage of 'Access' antibiotics prescribed.

Inequality in iron and folic acid consumption and dietary diversity in pregnant women following exposure to maternal nutrition interventions in three low- and middle-income countries.

Research is available on improved coverage and practices from several large-scale maternal nutrition programmes, but not much is known on change in inequalities. This study analyses wealth and education inequality using Erreygers and Concentration indices for four indicators: adequate iron and folic acid (IFA) consumption, women's dietary diversity, and counselling on IFA and dietary diversity. A pre-test-post-test, control group design. Maternal nutrition intervention programmes conducted in Bangladesh, Burkina Faso and Ethiopia during 2015-2022. Recently delivered women (RDW) and pregnant women (PW). Statistically significant reductions in education inequality were observed for adequate IFA consumption, counselling on IFA and dietary diversity in intervention areas of Bangladesh and for adequate IFA consumption in intervention areas of Burkina Faso.A significant decrease in wealth inequality was observed for adequate IFA consumption in the intervention areas of Bangladesh, whereas a significant increase was observed in the non-intervention areas for counselling on IFA in Ethiopia and for dietary diversity in Burkina Faso. The results can be attributed to the extensive delivery system at community level in Bangladesh and being predominantly facility-based in Burkina Faso and Ethiopia. COVID-19 disruptions (in Burkina Faso and Ethiopia) and indicator choice also had a role in the results.The main takeaways for nutrition programmes are as follows: (a) assessing inequality issues through formative studies during designing, (b) monitoring inequality indicators during implementation, (c) diligently addressing inequality through targeted interventions, setting aside resources and motivating frontline workers to reduce disparities and (d) making inequality analysis a routine part of impact evaluations.

Quantized Magnetic Domain Wall Synapse for Efficient Deep Neural Networks.

The quantization of synaptic weights using emerging nonvolatile memory (NVM) devices has emerged as a promising solution to implement computationally efficient neural networks on resource constrained hardware. However, the practical implementation of such synaptic weights is hampered by the imperfect memory characteristics, specifically the availability of limited number of quantized states and the presence of large intrinsic device variation and stochasticity involved in writing the synaptic states. This article presents on-chip training and inference of a neural network using quantized magnetic domain wall (DW)-based synaptic array and CMOS peripheral circuits. A rigorous model of the magnetic DW device considering stochasticity and process variations has been utilized for the synapse. To achieve stable quantized weights, DW pinning has been achieved by means of physical constrictions. Finally, VGG8 architecture for CIFAR-10 image classification has been simulated by using the extracted synaptic device characteristics. The performance in terms of accuracy, energy, latency, and area consumption has been evaluated while considering the process variations and nonidealities in the DW device as well as the peripheral circuits. The proposed quantized neural network (QNN) architecture achieves efficient on-chip learning with 92.4% and 90.4% training and inference accuracy, respectively. In comparison to pure CMOS-based design, it demonstrates an overall improvement in area, energy, and latency by 13.8 × , 9.6 × , and 3.5 × , respectively.