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25 articles for “energy-efficient memory”
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Development of Polymer Based SRAM Cell with Enhance Low Power Performance
Abstract: Low-power memory technologies are in high demand with the rapid growth of portable electronics and energy-efficient computing systems. Static random-access memory (SRAM) plays a crucial role in processors, cache memories, and system-on-chip applications due to their speed and reliability. Static Random Access Memory (SRAM) is typically implemented using complementary MOS (CMOS) technology, which integrates both PMOS (P-channel Metal–Oxide–Semiconductor) and NMOS (N-channel Metal–Oxide–Semiconductor) transistors. However, as technology scales to deep submicron …
Published in Journal of Polymer & Composites · Vol. 14, Issue 1, 2026 · pp. 1439–1448 Read article
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Implementation of STATIC-RANDOM-ACCESS-MEMORY-Based In-Memory Computing-architecture for improving Energy Efficiency
Abstract: The in-memory-computing architecture the improvement of big data and high-performance computing. In memory-computing (IMC) as reduces the latency and power consumption of data processing. Proposed research paper static random-access memory-based IMC architecture. By completing internal write-back, NMOS transistors increase computational efficiency and eliminate the need to read the computational output right away. A 128×128 STATIC-RANDOM-ACCESS-MEMORY-IMC macro chip is designed using the 78-nm technology. The energy efficiency of 55.3TOPS/W with supply …
Published in Journal of VLSI Design Tools and Technology · Vol. 15, Issue 1, 2025 · pp. 25–35 Read article
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Photonic-Assisted Spintronic Solid-State Switching Model for High-Speed Memory Devices
Abstract: The rapid advancement of high-speed computing and data-centric applications has intensified the demand for energy-efficient and ultra-fast memory technologies. This paper proposes a Photonic-Assisted Spintronic Solid-State Switching Model for next-generation high-speed memory devices. The proposed framework integrates photonic excitation mechanisms with spintronic switching dynamics to enhance data transfer speed, minimize switching delay, and reduce power dissipation in solid-state memory architectures. By combining optical pulse-assisted spin polarization with magnetic tunnel junction-based …
Published in International Journal of Solid State Innovations & Research · Vol. 4, Issue 1, 2026 Read article
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Ferroelectric Materials for Next-Generation Non-Volatile Memory Applications
Abstract: The continuous scaling of conventional memory technologies is increasingly constrained by limitations in power consumption, speed, endurance, and integration density. As data-intensive applications such as artificial intelligence, Internet of Things, and edge computing demand fast and energy-efficient memory solutions, alternative non-volatile memory technologies have gained significant attention. Ferroelectric materials, characterized by their reversible spontaneous polarization, offer a promising pathway toward next-generation non-volatile memory due to their intrinsic non-volatility, low operating …
Published in Journal of Microelectronics and Solid State Devices · Vol. 13, Issue 1, 2026 · pp. 1–9 Read article
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Reinforcement Learning for Adaptive Sensing with Shape Memory Polymer-Based IoT Nodes
Abstract: The rapid expansion of intelligent sensing in the Internet of Things (IoT) has revealed the pressing need for materials and algorithms capable of self-adaptation in volatile environments. Conventional polymer-based sensors and static control strategies often fail to capture nonlinear thermo-mechanical dynamics, leaving them unsuitable for unpredictable operating conditions. Although prior studies have improved polymer composites or introduced algorithmic optimization independently, few attempts have coupled the adaptability of smart materials with …
Published in Journal of Polymer & Composites · Vol. 13, Issue 6, 2025 · pp. 370–391 Read article
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Educating Compilers to Learn: Utilizing Machine Learning for More Brilliant Code Optimization
Abstract: This study explores the use of machine learning (ML) approaches to compiler optimization. The now-traditional static compilation techniques are transformed into adaptive, dynamic systems capable of making context-specific advancements. Traditional compilers rely mostly on heuristic or rule-based optimization techniques. While these techniques work well in general cases, they consistently fail to adapt well within the limits of code structures that modern machines display. This limitation is especially acute in today's …
Published in Journal of Computer Technology & Applications · Vol. 16, Issue 1, 2025 · pp. 50–54 Read article
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A High Frequency and Power Efficient Memristor Emulator and Its Application
Abstract: This study presents a small, energy-efficient memristor emulator made for use at high frequencies. The proposed circuit has a simple design that only includes three n-type MOSFETs and a grounded capacitor. This means that there is no need for active components or DC biasing. This streamlined architecture not only makes things easier, but it also uses very little power, with dynamic and static power measured at 15.82 μW and 65.6 …
Published in International Journal of Electrical and Communication Engineering Technology · Vol. 3, Issue 2, 2025 · pp. 45–52 Read article
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Cross-layer Solutions in WSN Routing: A Review
Abstract: WSN is a less infrastructure wireless network which is embedded with large number of Sensor Nodes (SN). Its ad-hoc manner of device distribution allows it to monitor conditions under physical and environmental scenarios. Typically, SNs in WSN are installed in a specified geographical location to monitor required information. Due to SNs’ self-configuring ability, the exploitation of target is simpler. Though, its functioning is limited with factors such as energy efficiency, …
Published in International Journal of Wireless Security and Networks · Vol. 1, Issue 2, 2023 · pp. 26–36 Read article
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An Investigative Study on Cache-Oblivious Data Structures
Abstract: Cache-oblivious data structures and data management systems have emerged as critical components in modern computing environments, aiming to optimize memory access patterns across different levels of the memory hierarchy without explicit knowledge of cache sizes or configurations. This study presents an overview of cache-oblivious techniques, including adaptive data structures, compression, parallel processing, and security considerations. The workexplores future directions in cache-oblivious systems, such as non-volatile memory support, graph processing, edge …
Published in International Journal of Data Structure Studies · Vol. 1, Issue 2, 2023 · pp. 33–37 Read article
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Delay Analysis of HS-14T 45nm CMOS SRAM with Different SRAM Techniques for Improvement in Speed
Abstract: This paper presents a novel HS-14T design aimed at significantly improving read and write delays compared to conventional 6T and other memory cells RHRD-12T, SEUH-12T, and NHRC-14T. As technology scales, the demand for faster and more stable memory cells becomes increasingly critical. Our proposed HS-14T incorporates additional transistors to enhance stability and reduce access times, resulting in notable performance gains. Comprehensive simulations reveal that the HS-14T SRAM cell substantially reduces …
Published in Journal of VLSI Design Tools and Technology · Vol. 14, Issue 2, 2024 · pp. 1–13 Read article
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Innovations in Polymer Chemistry for Smart Composite Materials in Building Applications
Abstract: Driven by the need to reduce maintenance costs and environmental impacts, researchers have developed smart polymers that not only sense damage but also autonomously respond to external stimuli such as temperature, mechanical stress, and light. In the built environment, these advanced polymers—particularly shape memory polymers (SMPs)—offer unique self-healing, self-sensing, and adaptive properties that enhance the durability, safety, and energy efficiency of structures. The emergence of smart polymers marks a significant …
Published in Journal of Polymer & Composites · Vol. 13, Issue 5, 2025 · pp. 699–707 Read article
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Advanced AI based Energy Monitoring and Demand Prediction with Theft Detection
Abstract: This paper presents a study on an AI-based energy management system, which is designed for real-time monitoring of energy consumption for theft detection and energy demand prediction. Our energy management system has voltage and current sensors for energy consumption measurement and provides real- time data on voltage (V), current (mA), and energy units. We have implemented Machine Learning algorithm SVM to improve the process of theft detection by identifying anomalies …
Published in Journal of Alternate Energy Sources & Technologies · Vol. 17, Issue 1, 2026 Read article
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Bridging Brain-Inspired Learning and Quantum Reasoning for Future AGI Systems
Abstract: This research paper presents a novel neuromorphic–quantum hybrid computing framework envisioned to advance intelligent systems toward artificial general intelligence. The architecture integrates brain-inspired spiking networks for adaptive, energy-efficient learning with quantum processors for non-classical optimization and reasoning. A shared synaptic–quantum memory layer enables dual information representation, while neuromorphic adaptive controllers provide real-time stabilization of noisy quantum circuits. While quantum processors offer features like superposition- enabled exploration and entanglement-based correlations that …
Published in Current Trends in Signal Processing · Vol. 16, Issue 1, 2026 Read article
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A Physics-Informed Graph Neural Network Framework for Real- Time Thermal-Aware Fault Prediction and Adaptive Power Optimization in Heterogeneous System-on-Chip Architectures
Abstract: Heterogeneous System-on-Chip (SoC) architectures are increasingly adopted in edge computing, artificial intelligence, autonomous systems, and high-performance embedded platforms due to their superior computational efficiency and flexibility. However, increasing integration density and workload diversity introduce severe thermal hotspots, accelerated device degradation, and unexpected hardware faults that adversely affect system reliability and energy efficiency. This study proposes a Physics-Informed Graph Neural Network (PI-GNN) framework for real-time thermal- aware fault prediction and adaptive …
Published in Journal of VLSI Design Tools and Technology · Vol. 16, Issue 2, 2026 Read article
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A Reviewed Study On Cpu-Optimized Parameter-Efficient Fine- Tuning For Large Language Models To Increase Accuracy Using Lora
Abstract: The fast proliferation of Large Language Models (LLMs) has increased the need to optimize the process of fine-tuning but the existing workflows that require a GPU are still expensive, intensive, and unavailable to most researchers. This paper is driven by the desire to have a more cost-efficient and democratized version by examining a CPU-efficient implementation of Parameter-Efficient Fine-Tuning (PEFT) based on Low-Rank Adaptation (LoRA). The major purpose of the study …
Published in Recent Trends in Parallel Computing · Vol. 13, Issue 1, 2026 Read article
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Innovative Applications of Smart Materials in Aviation: A Comprehensive Review
Abstract: Smart materials are transforming the aviation industry by offering innovative solutions that enhance performance, safety, and sustainability. The use of smart materials can enhance the efficiency, safety, and longevity of aerospace structures by sensing, responding, and adjusting to environmental changes in real time. The objective of this review is to examine how smart materials can be used in aviation, especially in the field of sensor networks, control systems, and structural …
Published in Journal of Polymer & Composites · Vol. 13, Issue 5, 2025 · pp. 114–132 Read article
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A Memory-Based Genetic Algorithm for Optimization of Power Generation in a Microgrid
Abstract: Due to advancement in power electronics field, it is becoming more feasible to integrate renewable energy into power grid. Renewable energy sources are prompting more and more small investors to invest in generation and distribution of renewable energy at microgrid level. The increased competition requires energy producers to offer energy at minimum possible cost to gain the confidence of consumers, which needs efficient methods to schedule energy generation among the …
Published in Journal of Semiconductor Devices and Circuits · Vol. 12, Issue 3, 2025 · pp. 29–38 Read article
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Comparative Analysis of AI-Based Approach vs. Traditional Methods in Climate Modeling
Abstract: Climate modeling helps to predict the future of climate variations and human interference with environment. The traditional General Circulation Models (GCMs) are based on physics-derived mathematical equations but are very expensive in terms of computation. There are alternative ways to perform climate modeling in recent years with the rise and improvement of Artificial Intelligence (AI) based approaches in term of predictability, efficiency, and classification of extreme events compared to conventional. …
Published in Current Trends in Information Technology · Vol. 15, Issue 3, 2025 · pp. 26–32 Read article
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CMOS-Based Process-Scalable Analog Circuits for Machine Learning: A Comprehensive Review and Future Directions.
Abstract: Analog computing techniques are gaining attention for machine learning (ML) applications due to their ability to reduce computational complexity. Continuous operations such as addition and subtraction offer a simpler and more efficient approach compared to probabilistic product decoding, which can be sensitive to noise and inconsistent measurements. This paper presents a simulated VLSI implementation of a broadcast edge connection, independent of the MOS component model, along with experimental results. The …
Published in Journal of VLSI Design Tools and Technology · Vol. 15, Issue 1, 2025 · pp. 8–17 Read article
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Multi-Parameter Biomedical Sensor-Based Mental State Classification Using EEG And Deep Learning Techniques
Abstract: With mental health concerns becoming increasingly widespread, there is a strong need for systems that can monitor conditions like stress, anxiety, and fatigue in a continuous and non- invasive manner. This research proposes a novel multi-parameter biomedical sensing framework for mental state classification by integrating electroencephalography (EEG) signals with physiological parameters, including body temperature acquired using LM35 sensors, heart rate from pulse sensors, and blood oxygen saturation (SpO₂) measurements. The …
Published in Current Trends in Signal Processing · Vol. 16, Issue 2, 2026 Read article