Low-power Processing Research Articles

Low power process tolerant nano cache memory for military applications

ABSTRACT Rapid growth in high-performance battery-powered computing applications, Internet of Things (IoT) and artificial intelligence (AI), has raised the need for low-power integrated circuits. Particularly in military applications, wireless sensor nodes are used to detect border intrusion, weapon attacks, biometric wearables, internet of battlefield things (IoBT), etc. The computing and security devices utilised by the soldiers are powered by batteries. To increase the life and performance of these devices, the integrated circuits (IC) used within these devices need careful design. Particularly, an appropriate memory needs more attention because the design of memory has a substantial impact on IC performance as it occupies most of the chip space. In this article, a novel 8T transistor low-power cache memory cell is proposed and simulations for nominal chirality and dual chirality are done using HSPICE-compatible CNFET (carbon nanotube field effect transistor) technology. The power consumption is minimized in dual chirality case than nominal chirality. But still the power saving percentage of the proposed cache memory cell remains good compared to conventional memory structures. Thus the proposed cache memory cell provides low power, delay and energy apart from being process tolerant, thus proving that it is compatible for warfare and military applications.

FPGA Implementation of Compact Architecture for Lightweight Hash Algorithm for Resource Constrained Devices

The security implications of the emerging world of smart devices brought about by the Internet of things' recent rise are enormous. IoT devices typically have restricted resources, such as minimal memory, low processing power, and a short battery life, in addition to their stated security level. In the context of IoT, lightweight cryptographic primitives are presented taking into account the trade-off between speed and security assurance. In this research, we proposed lightweight cryptographic hash methods and optimized hardware for devices with limitations. The size, speed, efficiency, and power consumption of our suggested solutions are examined on FPGA systems. Bit permutation, linear transformation, and S-Box functionality are used in the suggested design. To analyze large data sets and Internet of Things applications, conventional hash algorithms need memory and time. Thus, the demand arises for a lightweight cryptographic protocol that is both rapid and safe. This technique offers the necessary security criteria of conventional hash functions and the parameters for lightweight cryptographic protocols. The proposed architecture is tested and validated based on three key criteria: memory, speed, and power consumption, which are important factors in determining its lightweight nature. Based on the findings, the proposed architecture outperforms other lightweight protocols regarding memory usage, performance, and power consumption. For an appropriate FPGA-based application deployment, a thorough comparison of our suggested designs is detailed on various FPGA families.

LSTM Gate Disclosure as an Embedded AI Methodology for Wearable Fall-Detection Sensors

In this paper, the concept of symmetry is used to design the efficient inference of a fall-detection algorithm for elderly people on embedded processors—that is, there is a symmetric relation between the model’s structure and the memory footprint on the embedded processor. Artificial intelligence (AI) and, more particularly, Long Short-Term Memory (LSTM) neural networks are commonly used in the detection of falls in the elderly population based on acceleration measures. Nevertheless, embedded systems that may be utilized on wearable or wireless sensor networks have a hurdle due to the customarily massive dimensions of those networks. Because of this, the algorithms’ most popular implementation relies on edge or cloud computing, which raises privacy concerns and presents challenges since a lot of data need to be sent via a communication channel. The current work proposes a memory occupancy model for LSTM-type networks to pave the way to more efficient embedded implementations. Also, it offers a sensitivity analysis of the network hyper-parameters through a grid search procedure to refine the LSTM topology network under scrutiny. Lastly, it proposes a new methodology that acts over the quantization granularity for the embedded AI implementation on wearable devices. The extensive simulation results demonstrate the effectiveness and feasibility of the proposed methodology. For the embedded implementation of the LSTM for the fall-detection problem on a wearable platform, one can see that an STM8L low-power processor could support a 40-hidden-cell LSTM network with an accuracy of 96.52%.

YOLO-Faster: An efficient remote sensing object detection method based on AMFFN.

As a pivotal task within computer vision, object detection finds application across a diverse spectrum of industrial scenarios. The advent of deep learning technologies has significantly elevated the accuracy of object detectors designed for general-purpose applications. Nevertheless, in contrast to conventional terrestrial environments, remote sensing object detection scenarios pose formidable challenges, including intricate and diverse backgrounds, fluctuating object scales, and pronounced interference from background noise, rendering remote sensing object detection an enduringly demanding task. In addition, despite the superior detection performance of deep learning-based object detection networks compared to traditional counterparts, their substantial parameter and computational demands curtail their feasibility for deployment on mobile devices equipped with low-power processors. In response to the aforementioned challenges, this paper introduces an enhanced lightweight remote sensing object detection network, denoted as YOLO-Faster, built upon the foundation of YOLOv5. Firstly, the lightweight design and inference speed of the object detection network is augmented by incorporating the lightweight network as the foundational network within YOLOv5, satisfying the demand for real-time detection on mobile devices. Moreover, to tackle the issue of detecting objects of different scales in large and complex backgrounds, an adaptive multiscale feature fusion network is introduced, which dynamically adjusts the large receptive field to capture dependencies among objects of different scales, enabling better modeling of object detection scenarios in remote sensing scenes. At last, the robustness of the object detection network under background noise is enhanced through incorporating a decoupled detection head that separates the classification and regression processes of the detection network. The results obtained from the public remote sensing object detection dataset DOTA show that the proposed method has a mean average precision of 71.4% and a detection speed of 38 frames per second.

An approach to interpreting metastable austenitic material sensors for fatigue analysis

The transformation of metastable austenite to martensite under mechanical loading can be harnessed to create a material sensor which records a measure of the load history without the need for electrical energy and can be read out at arbitrary intervals via eddy current probing, thus leading to an ultra-low-power sensing solution. This paper presents possibilities of processing this load amplitude-dependent evolution of martensite content loading for component fatigue analysis. The general method is based on using a theoretical material model typically used in finite element analyses which includes hardening plasticity and phase transformation to precompute tables of stress amplitude or cumulative damage corresponding to different sensor readings which can be stored on a low power processing system onboard the component for energy-efficient lookup. At nominal single amplitude loading, the sensor can be used as a load cycle counter for known loads or as an overload detection device upon divergent martensite content rise. Interpretation of block program loading is less practical due to resolution issues. Under random loading, sequence effects get averaged out; interpretation is easiest with narrow load spectra, but information can be gained from very wide spectra as well. Multiple sensors at different locations can aid interpretation. Uncertainty due to necessary assumptions and untreated influences of temperature and loading rate is discussed.

Model and Implementation of a Novel Heat-Powered Battery-Less IIoT Architecture for Predictive Industrial Maintenance

The research and management of Industry 4.0 increasingly relies on accurate real-time quality data to apply efficient algorithms for predictive maintenance. Currently, Low-Power Wide-Area Networks (LPWANs) offer potential advantages in monitoring tasks for predictive maintenance. However, their applicability requires improvements in aspects such as energy consumption, transmission range, data rate and constant quality of service. Commonly used battery-operated IIoT devices have several limitations in their adoption in large facilities or heat-intensive industries (iron and steel, cement, etc.). In these cases, the self-heating nodes together with the appropriate low-power processing platform and industrial sensors are aligned with the requirements and real-time criteria required for industrial monitoring. From an environmental point of view, the carbon footprint associated with human activity leads to a steady rise in global average temperature. Most of the gases emitted into the atmosphere are due to these heat-intensive industries. In fact, much of the energy consumed by industries is dissipated in the form of waste heat. With this scenario, it makes sense to build heat transformation collection systems as guarantors of battery-free self-powered IIoT devices. Thermal energy harvesters work on the physical basis of the Seebeck effect. In this way, this paper gathers the methodology that standardizes the modelling and simulation of waste heat recovery systems for IoT nodes, gathering energy from any hot surface, such as a pipe or chimney. The statistical analysis is carried out with the data obtained from two different IoT architectures showing a good correlation between model simulation and prototype behaviour. Additionally, the selected model will be coupled to a low-power processing platform with LoRaWAN connectivity to demonstrate its effectiveness and self-powering ability in a real industrial environment.

공격 행동 인식 및 중재를 위한 IMU 기반 웨어러블 시스템 개발

The biggest type of behavior that prevents people with developmental disabilities from entering society is aggressive behavior. Aggressive behavior can pose a threat not only to the personal safety of the person with a developmental disability, but also to the physical safety of others. In this study, we propose a wearable system using a low-power processor. The proposed system uses an IMU (Inertial Measurement Unit) to analyze user behavior, and when attack behavior is not detected for a certain period of time through an LED array attached to the developed system, an interesting LED is displayed. By expressing patterns, we provide behavioral intervention through compensation to people with developmental disabilities. In order to implement a system that must be worn for a long time in a power-limited environment, we present a method to optimize performance and energy consumption across all stages, from data preprocessing to AI model application.

Target recognition in diverse synthetic aperture radar image datasets with low size weight and power processing hardware

Target recognition in diverse synthetic aperture radar image datasets with low size weight and power processing hardware

A Novel Source Code Representation Approach Based on Multi-Head Attention

Code classification and code clone detection are crucial for understanding and maintaining large software systems. Although deep learning surpasses traditional techniques in capturing the features of source code, existing models suffer from low processing power and high complexity. We propose a novel source code representation method based on the multi-head attention mechanism (SCRMHA). SCRMHA captures the vector representation of entire code segments, enabling it to focus on different positions of the input sequence, capture richer semantic information, and simultaneously process different aspects and relationships of the sequence. Moreover, it can calculate multiple attention heads in parallel, speeding up the computational process. We evaluate SCRMHA on both the standard dataset and an actual industrial dataset, and analyze the differences between these two datasets. Experiment results in code classification and clone detection tasks show that SCRMHA consumes less time and reduces complexity by about one-third compared with traditional source code feature representation methods. The results demonstrate that SCRMHA reduces the computational complexity and time consumption of the model while maintaining accuracy.

Integrated photonic encoder for low power and high-speed image processing.

Modern lens designs are capable of resolving greater than 10 gigapixels, while advances in camera frame-rate and hyperspectral imaging have made data acquisition rates of Terapixel/second a real possibility. The main bottlenecks preventing such high data-rate systems are power consumption and data storage. In this work, we show that analog photonic encoders could address this challenge, enabling high-speed image compression using orders-of-magnitude lower power than digital electronics. Our approach relies on a silicon-photonics front-end to compress raw image data, foregoing energy-intensive image conditioning and reducing data storage requirements. The compression scheme uses a passive disordered photonic structure to perform kernel-type random projections of the raw image data with minimal power consumption and low latency. A back-end neural network can then reconstruct the original images with structural similarity exceeding 90%. This scheme has the potential to process data streams exceeding Terapixel/second using less than 100 fJ/pixel, providing a path to ultra-high-resolution data and image acquisition systems.

DATA SECURITY IN COMMUNICATION USING BLOCKCHAIN AND KEY BASED PROTOCOL

Satellite communications play an important role in the development of global communications networks. Recently, satellite networking has been receiving a lot of attention as a solution to alleviate the limitations of terrestrial networks due to low stability and coverage. It has many disadvantages, including low processing power, storage space, and limited data security. Illegal access has become a problem. Data processing power is minimal, storage space and data security are limited due to regional power availability and physical satellite limitations, and data can be modified or accessed by malicious users. As the importance of satellite communication increases in the development of global communication networks, concerns about the security of satellite communication are also growing. This project proposes a satellite communication network using blockchain technology and QKD protocol based on authentication and personal information protection. To achieve this goal, an architecture was built consisting of traditional and constrained devices connected to the blockchain over a wireless heterogeneous network. Communication uses registration, authentication, and cancellation. The satellite transmits the received data to a base station on the ground, which stores all key parameters in the decentralized blockchain and removes all invalid node certificates from the blockchain. The proposed blockchain and QKD satellite system provides a high level of security for future 6Gandbeyond networks, Internet of Things, autonomous vehicles and other rapidly developing applications.

Precise and low-power closed-loop neuromodulation through algorithm-integrated circuit co-design.

Implantable neuromodulation devices have significantly advanced treatments for neurological disorders such as Parkinson's disease, epilepsy, and depression. Traditional open-loop devices like deep brain stimulation (DBS) and spinal cord stimulators (SCS) often lead to overstimulation and lack adaptive precision, raising safety and side-effect concerns. Next-generation closed-loop systems offer real-time monitoring and on-device diagnostics for responsive stimulation, presenting a significant advancement for treating a range of brain diseases. However, the high false alarm rates of current closed-loop technologies limit their efficacy and increase energy consumption due to unnecessary stimulations. In this study, we introduce an artificial intelligence-integrated circuit co-design that targets these issues and using an online demonstration system for closed-loop seizure prediction to showcase its effectiveness. Firstly, two neural network models are obtained with neural-network search and quantization strategies. A binary neural network is optimized for minimal computation with high sensitivity and a convolutional neural network with a false alarm rate as low as 0.1/h for false alarm rejection. Then, a dedicated low-power processor is fabricated in 55 nm technology to implement the two models. With reconfigurable design and event-driven processing feature the resulting application-specific integrated circuit (ASIC) occupies only 5mm2 silicon area and the average power consumption is 142 μW. The proposed solution achieves a significant reduction in both false alarm rates and power consumption when benchmarked against state-of-the-art counterparts.

Suppressing substrate oxidation during plasma-enhanced atomic layer deposition on semiconductor surfaces

Plasma-enhanced atomic layer deposition (PE-ALD) is widely employed in microelectronics, energy, and sensing applications. Typically, PE-ALD processes for metal oxides utilize remote inductively coupled plasmas operated at powers of >200 W, ensuring a sufficient flux of oxygen radicals to the growth surface. However, this approach often leads to significant oxidation of chemically sensitive substrates, including most technological semiconductors. Here, we demonstrate that plasma powers as low as 5 W can effectively suppress substrate oxidation while maintaining the structural, optical, and electronic quality of the films. Specifically, we investigate the growth of titanium oxide (TiOx) using two commonly used metalorganic precursors, titanium isopropoxide and tetrakis(dimethylamino)titanium. Films deposited with 5 and 300 W oxygen plasma power are nearly indiscernible from one another, exhibiting significantly lower defect concentrations than those obtained from thermal ALD with H2O. The low plasma power process preserves desired physical characteristics of PE-ALD films, including large optical constants (n > 2.45 at 589 nm), negligible defect-induced sub-bandgap optical absorption (α < 102 cm−1), and high electrical resistivity (>105 Ω cm). Similar behavior, including suppressed interface oxidation and low defect content, is observed on both Si and InP substrates. As an example application of this approach, the assessment of InP/TiOx photocathodes and Si/TiOx photoanodes reveals a significant improvement in the photocurrent onset potential in both cases, enabled by suppressed substrate oxidation during low power PE-ALD. Overall, low power PE-ALD represents a generally applicable strategy for producing high quality metal oxide thin films while minimizing detrimental substrate reactions.

A Metastability Risk Prediction and Mitigation Technique for Clock-Domain Crossing With Single-Stage Synchronizer in Near-Threshold-Voltage Multivoltage/ Frequency-Domain Network-on-Chip

For a network-on-chip (NoC) with multiple voltage/frequency domains, metastability hurts the reliability during the clock-domain crossing, especially in the near-threshold-voltage (NTV) region. Conventional multistage synchronizers reduce the probability of metastability but have a high latency penalty. This article presents a technique titled metastability risk prediction and mitigation (MPAM) that predicts the near-future metastability risks by a triple-phase clock monitoring circuitry and mitigates them by a metastability-free clock scheme. Therefore, the MPAM enables only one flip-flop for data synchronization without degrading the reliability against metastability, thus improving the latency and throughput of NoC. We prototyped a 2-by-2 NoC test chip with four independent voltage/frequency domains in a 40-nm low-power (LP) process, featuring the MPAM technique. The measurement shows that the MPAM significantly reduces the metastability condition rate by 10 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$^{10}$</tex-math> </inline-formula> times under different clock frequency ratios. Moreover, by enabling only one flip-flop for synchronization, the MPAM-based NoC achieves packet latency reduction, throughput improvement, and energy efficiency gain by 58%, 13.4%, and 8.6%, respectively.

Experimental study of photovoltaic-thermoelectric generator with graphite sheet

This work attempts to propose a technique which constitutes a photovoltaic (PV) panel that incorporates a thermoelectric generator (TEG) on the back of the PV. When exposed to the sun for an extended period, heat is produced at the back side of PV panels, which is generally wasted. While this heat is being wasted, the efficacy of the PV panel goes down, which invariably invokes the need for cooling systems. Conventional cooling systems are efficient but require more area. As a result, the cost of the system grows with its size. These lagging remarks invoke designing a system with simple architecture, easy installation and simplifying processes while achieving effective outcomes. Owing to the limitations of the present techniques, a system with a simple design, rapid setup, and dependable outcomes with low processing power is needed. The thermoelectric generator is structurally simple and can be a reliable solution for wasted-heat utilisation. Furthermore, graphite is a viable choice for increasing the PV-TEG output in the proposed technique. Maximum TEG voltage is 13.515 V when a graphite sheet is an auxiliary heat dissipator. The maximum difference in temperature between the graphite sheet and the PV back side is 6.682 °C.

Ultra-Fast MPPT for Residential PV Systems With Low DC-Link Capacitance and Differential Power Processing

Ultra-Fast MPPT for Residential PV Systems With Low DC-Link Capacitance and Differential Power Processing

Study of Existing Combination Circuits using CMOS and QCA Technology to Compare Their Efficacy

The potential limitations on the future advancement of microelectronics regular circuit scaling may arise from high power consumption and design intricacy. Therefore, it is imperative to introduce elective innovation to enhance future designs. The utilization of QCA for combinational circuit implementation represents a cutting-edge technological advancement that aims to address the scaling challenges faced by modern electronic devices. The circuit is characterized by the configuration of quantum cells in this logic design technique. This technique relies on the concept of computing through a field connection The utilization of QCA offers many benefits, such as reduced spatial requirements, elimination of interconnects, increased clock frequency, and the potential for low-power processing due to the absence of electron movement. This paper offers a comprehensive comparative analysis of various combinational circuits such as Full adder, MUX, XOR etc. that are considered essential in the field, by utilizing CMOS innovation and QCA nanotechnology.

Heterogeneous GNN-RL-Based Task Offloading for UAV-Aided Smart Agriculture

Having unmanned aerial vehicles (UAVs) with edge computing capability hover over smart farmlands supports Internet of Things (IoT) devices with low processing capacity and power to accomplish their deadline-sensitive tasks efficiently and economically. In this work, we propose a graph neural network-based reinforcement learning solution to optimize the task offloading from these IoT devices to the UAVs. We conduct evaluations to show that our approach reduces task deadline violations while also increasing the mission time of the UAVs by optimizing their battery usage. Moreover, the proposed solution has increased robustness to network topology changes and is able to adapt to extreme cases, such as the failure of a UAV.

Efficient Disease Identification Method for Crop Leaf using Deep Learning Techniques

Many prime grain-producing nations have implemented steps to limit export of grains as COVID-19 has expanded over the globe; food security has sparked significant worry from a number of stakeholders. One of the most crucial concerns facing all nations is how to increase grain output. However, the diseases occur in crops remain a challenge for countless farmers, therefore it is critical to understand their severity promptly and precisely to guide the them in taking additional measures to lessen the chances of plants being affected furthermore.&#x0D; This paper describes a deep learning model for the identification of crop diseases that can achieve high accuracy with low processing power. The model, called the inception v3 network, has been tested on a tomato leaf dataset and has obtained a average identification accuracy of 98.00% and further the ensemble of two inception v3 models with slight diversity achieved an accuracy of 98.11%. The results suggest that this model could be useful in improving food security by helping farmers quickly and accurately identify crop diseases and take appropriate measures to prevent further spread.

Secure and Transparent Supply Chain Management using Blockchain and IoT

Blockchain technology has emerged as a disruptive force across various industries, and its integration with the Internet of Things (IoT) has unlocked new avenues for supply chain management. The conventional supply chain systems often encounter challenges related to privacy, security, and data integrity. In contrast, blockchain's decentralized and tamper-proof nature ensures a secure, auditable, and transparent record of product movement within the supply chain. By leveraging the immutable properties of blockchain, the system enhances product traceability, authenticity, and accountability while significantly reducing operational costs. IoT devices are vulnerable to attack as due to low processing power, storage limitations etc. Blockchain integrated with IoT provides a solution faced by the several industries. Blockchains and smart contracts are technology that has gained massive attention. The integration of blockchain addresses these shortcomings by providing robust data security and integrity, minimizing the risk of unauthorized access or alteration. This paper presents a system that helps the industrialist to have an access to agricultural data and supply of crops data to farmer. As industries continue to embrace digitization and connectivity, the presented system offers a significant step towards a more streamlined and secure future for agricultural information sharing. This system will be effective for the supply chain management for the trusted delivery.