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This paper presents a shock detection device for packages in the supply chain. The primary purpose is to identify package damage during storage, delivery, and handling. Additionally, products are likely to be damaged if dropped from a certain height, which sometimes does not appear on the package. By continuously measuring package vibrations and detecting shocks in the supply chain, consumers can gain an insight into the state of the product upon delivery. This paper presents the Smart Sticker implementation for ultra-low-power shock detection in the supply chain. The overall energy consumption must be kept as low as possible while continuously sensing the presence of shock to ensure that the Smart Sticker's battery lasts as long as possible. The Smart Sticker functions in three modes to meet the established constraints: low-power, active, and data transfer mode. While detecting the shock, the low-power mode uses the least amount of energy needed. If the shock exceeds the threshold, the Smart Sticker enters active mode, stores the detected g force value in memory, and then switches back to low-power mode. Finally, employing Near Field Communication (NFC) and energy harvesting, the data transfer mode allows the consumer to read the recorded data. The results show that the Smart Sticker for shock detection performs according to set requirements and successfully monitors and detects shock for packages in the supply chain. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) and blockchain are emerging technologies that have attracted attention in many industries, including healthcare, automotive, and supply chain. IoT networks and devices are typically low-powered and susceptible to cyber intrusions. However, blockchains hold considerable potential for securing low-power IoT networks. Blockchain networks provide security features such as encryption, decentralisation, time stamps, and ledger functions. The integration of blockchain and IoT technologies may address many of the security concerns. However, integrating blockchain with IoT raises several issues, including the security vulnerabilities and anomalies of blockchain-based IoT networks. In this paper, we report on our experiments using our blockchain test bed to demonstrate that blockchains on IoT platforms are vulnerable to DDoS attacks, which can also potentially lead to device hardware failures. We show that a number of anomalies are visible during either a DDoS attack or IoT device failure. In particular, the temperature of IoT hardware devices can exceed 90 °C during a DDoS attack, which could lead to hardware failure and potential fire hazards. We also found that the Block Transaction Rate (BTR) and network block loss percentage can increase due to corrupted hardware, with the BTR dropping to nearly zero blocks/sec and a block loss percentage of over 50 percent for all evaluated blockchains, and as high as 81.3 percent in one case. Our experiments demonstrate that anomalous temperature, latency, bandwidth, BTR, and network block loss percentage can potentially be used to identify DDoS attacks. [ABSTRACT FROM AUTHOR]

Edge computing enables prompt responses in IoT environments, such as the operation of autonomous vehicles and unmanned aerial vehicles. However, with the increase in sensor nodes, the computational burden on the computing node also increases. Specifically, data filtering and reduction at application nodes add to the energy burden for battery-operated devices. In this paper, we propose a preprocessing system at the application node that requires low power consumption for data transmission reduction. Based on our simulations, we identify the minimum data size needed to preserve the signal. We first design the preprocessing system using a hardware description language to evaluate its performance. Then, we implement the open-library-based MCU system, including the proposed preprocessing IP, to assess its operation and overhead. Our implementation of the preprocessing system reduces data transmission by 50% with acceptable information loss. Additionally, the area and power consumption after the logic synthesis of the preprocessing IP within the entire MCU system are evaluated at only 3.6% and 13.1%, respectively. By performing preprocessing using the MCU and proposed IP, nearly 74.4% power reduction is achieved compared to using the existing MCU core. [ABSTRACT FROM AUTHOR]

Brain-inspired flexible neuromorphic devices are of great significance for next-generation high-efficiency wearable sensing and computing systems. In this paper, we propose a flexible organic electrochemical transistor using poly[(bithiophene)-alternate-(2,5-di(2-octyldodecyl)- 3,6-di(thienyl)-pyrrolyl pyrrolidone)] (DPPT-TT) as the organic semiconductor and poly(methyl methacrylate) (PMMA)/LiClO4 solid-state electrolyte as the gate dielectric layer. Under gate voltage modulation, an electric double layer (EDL) forms between the dielectric layer and the channel, allowing the device to operate at low voltages. Furthermore, by leveraging the double layer effect and electrochemical doping within the device, we successfully mimic various synaptic behaviors, including excitatory post-synaptic currents (EPSC), paired-pulse facilitation (PPF), high-pass filtering characteristics, transitions from short-term plasticity (STP) to long-term plasticity (LTP), and demonstrate its image recognition and storage capabilities in a 3 × 3 array. Importantly, the device's electrical performance remains stable even after bending, achieving ultra-low-power consumption of 2.08 fJ per synaptic event at −0.001 V. This research may contribute to the development of ultra-low-power neuromorphic computing, biomimetic robotics, and artificial intelligence. [ABSTRACT FROM AUTHOR]

Bluetooth Low Energy Mesh (BLE Mesh) enables Bluetooth flexibility and coverage by introducing Low-Power Nodes (LPNs) and enhanced networking protocol. It is also a commonly used communication method in sensor networks. In BLE Mesh, LPNs are periodically woken to exchange messages in a stop-and-wait way, where the tradeoff between energy and efficiency is a hard problem. Related works have reduced the energy consumption of LPNs mainly in the direction of changing the bearer layer, improving time synchronization and broadcast channel utilization. These algorithms improve communication efficiency; however, they cause energy loss, especially for the LPNs. In this paper, we propose a constrained flooding algorithm based on time series prediction and lightweight GBN (Go-Back-N). On the one hand, the wake-up cycle of the LPNs is determined by the time series prediction of the surrounding load. On the other, LPNs exchange messages through lightweight GBN, which improves the window and ACK mechanisms. Simulation results validate the effectiveness of the Time series Prediction and LlightWeight GBN (TP-LW) algorithm in energy consumption and throughput. Compared with the original algorithm of BLE Mesh, when fewer packets are transmitted, the throughput is increased by 214.71%, and the energy consumption is reduced by 65.14%. [ABSTRACT FROM AUTHOR]

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Modern processors often face challenges when handling instructions that overwhelm the branch target buffer (BTB), leading to front-end bottlenecks. As the BTB's capacity increases, its prediction module can become slower and power-hungry. In this paper, we introduce a straightforward yet highly effective two-level prediction mechanism to mitigate the escalating power consumption in the BTB structure, achieved by reducing the number of accesses. Our approach incorporates two main elements: M-BTB and V-BTB. M-BTB encompasses the first-level prediction mechanism and a fully associative BTB, while V-BTB houses the second-level prediction mechanism and a set-associative BTB. To implement our prediction mechanism, we optimize the traditional two-level BTB structure. The first level employs the skew mechanism, and the second level dissects the tag bits to create the Partial tag. These two levels of prediction mechanism correspond to the bank/way prediction for the two-level BTB structure. Our experimental results show that the first-stage prediction mechanism reduces M-BTB accesses by 75%, while the second-stage prediction mechanism ensures that over 98% of addresses require just zero or one way of the V-BTB to achieve a hit result. Our proposed approach achieves a remarkable 86–97% reduction in power consumption, with a minimal impact on performance and an increase in overall area efficiency. [ABSTRACT FROM AUTHOR]

The constant growth of social media, unconventional web technologies, mobile applications, and Internet of Things (IoT) devices create challenges for cloud data systems in order to support huge datasets and very high request rates. NoSQL databases, such as Cassandra and HBase, and relational SQL databases with replication, such as Citus/PostgreSQL, have been used to increase horizontal scalability and high availability of data store systems. In this paper, we evaluated three distributed databases on a low-power low-cost cluster of commodity Single-Board Computers (SBC): relational Citus/PostgreSQL and NoSQL databases Cassandra and HBase. The cluster has 15 Raspberry Pi 3 nodes with Docker Swarm orchestration tool for service deployment and ingress load balancing over SBCs. We believe that a low-cost SBC cluster can support cloud serving goals such as scale-out, elasticity, and high availability. Experimental results clearly demonstrated that there is a trade-off between performance and replication, which provides availability and partition tolerance. Besides, both properties are essential in the context of distributed systems with low-power boards. Cassandra attained better results with its consistency levels specified by the client. Both Citus and HBase enable consistency but it penalizes performance as the number of replicas increases. [ABSTRACT FROM AUTHOR]

Digital Image Processing has dominated Digital Signal Processing at the cost of more memory, resources, and high computational power. In image processing, filtering transformations and other operations need complex multiplications, and the multiplier is one of the most resources consuming elements. Recently, mitigating the multiplier complexity in the digital signal processing (DSP) algorithms sigma-delta modulation based general purpose and adaptive DSP algorithms are developed in MATLAB and compared with its counterpart multibit algorithms for functionality and area-performance-power in FPGA. The contemporary multiplier algorithms are also optimized to overcome the multiplier complexity challenge as computation becomes simple and fast. This paper extends the reported work by investigating the sigma-delta modulation approaches for developing a computationally efficient low-power image processing algorithm. The proposed model is designed, developed, and simulated in MATLAB. The simulation results are analyzed using SNR, MSE, and Peak SNR. The simulation results show that the proposed system can better mitigate the noise effect, making it robust for noisy environment. [ABSTRACT FROM AUTHOR]

This paper presents a CNFET (Carbon Nano-tube FET) based MT (Multi-Threshold)-SRAM (Static Random Access Memory) design based on the leakage reduction mechanism. A multi-threshold logic is employed for reducing the leakage current during read/write operations. Here, the multi-threshold technique is used to insert the high threshold sleep control to the low threshold circuit. The insertion is performed in a serial manner. The high threshold transistors are very useful for deriving the low sub-threshold current. Meanwhile, the low threshold transistors are promising for improving the circuit performance. The high-low threshold transistor pairs are used to change the channel length by modifying the oxide thickness of the transistors. The overall implementation of the Multi-threshold-based SRAM cells are implemented with the help of CNFET in-order to avoid the short channel effect, mobility degradation which is occurred while considering the channel length below 32 nm in CMOS (Complementary Metal Oxide Semiconductor) devices. The paper clearly represents the performance improvement of the proposed SRAM cells with above-mentioned technologies. [ABSTRACT FROM AUTHOR]

The survival rate for sudden cardiac arrest (SCA) is low, and patients with long-term risks of SCA are not adequately alerted. Understanding SCA's characteristics will be key to developing preventive strategies. Many lives could be saved if SCA's early onset could be detected or predicted. Monitoring heart signals continuously is essential for diagnosing sporadic cardiac dysfunction. An electrocardiogram (ECG) can be used to continuously monitor heart function without having to go to the hospital. A zeolite-based dry electrode can provide safe on-skin ECG acquisition while the subject is out-of-hospital and facilitate long-term monitoring. To the ECG signal, a low-power 1 μW read-out circuit was designed and implemented in our prior work. However, having long-term ECG monitoring outside the hospital, i.e., high battery life, and low power consumption while transmission and reception of ECG signal are crucial. This paper proposes a prototype with a 10-bit resolution ADC and nRF24L01 transceivers placed 5 m apart. The system uses the 2.4 GHz worldwide ISM frequency band with GFSK modulation to wirelessly transmit digitized ECG bits at 250 kbps data rate to a physician's computer (or similar) for continuous monitoring of ECG signals; the power consumption is only 11.2 mW and 4.62 mW during transmission and reception, respectively, with a low bit error rate of ≤0.1%. Additionally, a subject-wise cross-validated, three-fold, optimized convolutional neural network (CNN) model using the Physionet-SCA dataset was implemented on NVIDIA Jetson to identify the irregular heartbeats yielding an accuracy of 89% with a run time of 5.31 s. Normal beat classification has an F1 score of 0.94 and a ROC score of 0.886. Thus, this paper integrates the ECG acquisition and processing unit with low-power wireless transmission and CNN model to detect irregular heartbeats. [ABSTRACT FROM AUTHOR]

This paper proposes a novel analog front end to filter edges in captured images. The proposed architecture employs the Sobel algorithm and uses a 3 × 3 kernel to process the input images. We used 180 nm Semi-Conductor Laboratory (SCL) CMOS fabrication technology for implementation with a power supply of 1.8 V. The proposed design employs six analog differential pairs in each horizontal and vertical gradient unit. The inputs to the differential pairs are the active signals fed from the 3 × 3 kernel using the Sobel weight masks. We connected the differential datum pairs in cascade to estimate the gradient index in horizontal/vertical direction using the net difference current between the output terminals. The horizontal and vertical gradient units, viz., G H and G V , respectively, are configured based on the polarity of weights in respective Sobel masks. The gradient (G) of the processing pixel (PP) is estimated using the net difference current in the combined circuitry employing G H and G V cells. The key advantages of the proposed design are very low area realization, high accuracy, and low power dissipation. The efficacy of the proposed edge filter analog front end is estimated through simulations and analysis with two sets of test signals in the processing window. [ABSTRACT FROM AUTHOR]

This paper presents radiation hardened SRAM (namely LPDNUR and HSDNUR), both of which can self-recover from single-node and double-node upsets. LPDNUR uses a two-input C-element structure, which reduces the average power consumption because the stacked effect. Moreover, in order to reduce read access time (RAT) and write access time (WAT), the paper proposes HSDNUR, which uses a combination of NMOS and PMOS as the transistor for one-node data transmission, an approach that increases the current drive capability and reduces transmission delays. Compared to the state-of-the-art radiation-hardened SRAM design, such as QUCCE10T, QUCCE12T, We-Quatro, etc. The model results show that the average power consumption of LPDNUR is reduced by 24.59% on average, and the RAT and WAT of HSDNUR are reduced by an average of 35.71% and 24.14% respectively. • LPDNUR and HSDNUR fully achieve SNU and DNU recovery. • Average power of LPDNUR is 24.59% lower on average than other SRAM hardened since stack structure. • WAT and RAT of HSDNUR are 24.14% and 35.71% lower than other SRAM hardened on average by using TG. [ABSTRACT FROM AUTHOR]

This paper proposes a modified Schmitt-trigger (ST)-based single-ended 11 T (MST11T) SRAM cell. The proposed cell is best suited to ultra-low voltage applications. Two ST-based cross-coupled inverters comprise the storage cell of the proposed MST11T bit cell. In comparison with conventional inverters, ST-based inverters have sharp voltage transfer characteristics. As a result, the proposed bit cell's stability performance increases. The proposed SRAM cell's leakage power consumption is reduced because of the use of stacked N-type transistors. For the read operation of the proposed bit cell, the read decoupled technique is used. As a result, the read static noise margin (RSNM) has greatly improved. The proposed bit cell's write static noise margin (WSNM) is increased by adopting feedback-cutting methodology. The performance of the proposed bit cell is compared with that of conventional 6 T, conventional 8 T, Schmitt-trigger 10 T (known as ST2), modified PMOS-PMOS-NMOS-based cell core 10 T (MPPN10T), feedback-cutting 11 T (FC11T), Schmitt-trigger 11 T (ST11), and Schmitt-trigger 12 T (ST12T) cells. According to the simulation results, the proposed MST11T SRAM cell has RSNM of 2.42, 1.18, 1.71, 1.30, and 1.80 times higher when compared to 6 T, FC11T, ST2, MPPN10T, and ST12T, respectively. The WSNM of the proposed bit cell has been increased by 1.56, 2.44, 1.28, 1.71, 1.35, 1.52, and 1.02 times, respectively, over 6 T, 8 T, ST2, MPPN10T, FC11T, ST11T, and ST12T. Furthermore, the suggested cell has a read delay that is 1.32, 1.79, and 1.53 times lower than ST11T, FC11T, and ST12T, respectively. The proposed bit cell has a write delay that is 1.14 and 1.63 times lower than FC11T and ST11T, respectively. The proposed MST11T bit-cell consumes 3.74, 1.56, 4.59, 5.38, and 4.83 times less leakage power than the 8 T, ST2, MPPN10T, FC11T, and ST12 bit-cells, respectively. When compared to 8 T/ST2/MPPN10T/ST12T at 0.2 V supply voltage, the enhanced facts incur a 4.87/3.79/3.78/1.97 penalty in write delay. The figure of merit (FOM) is derived as a result of this extensive access to the revolutionary SRAM cell performance, i.e., offering greater values at a 0.2 V DC supply voltage. In addition, the paper examines the impact of manufacturing process and temperature changes on MST11T cell enactment, as well as the circuit robustness using HSPICE with 7-nm FinFET technology. [ABSTRACT FROM AUTHOR]

This paper investigates the use of approximate fixed-width and static segment multipliers in the design of Delayed Least Mean Square (DLMS) adaptive filters based on the sign–magnitude representation for the error signal. The fixed-width approximation discards part of the partial product matrix and introduces a compensation function for minimizing the approximation error, whereas the static segmented multipliers reduce the bit-width of the multiplicands at runtime. The use of sign–magnitude representation for the error signal reduces the switching activity in the filter learning section, minimizing power dissipation. Simulation results reveal that, by properly sizing the two approaches, a steady state mean square error practically unchanged with respect to the exact DLMS can be achieved. The hardware syntheses in a 28 nm CMOS technology reveal that the static segmented multipliers perform better in the learning section of the filter and are the most efficient approach to reduce area occupation, while the fixed-width multipliers offer the best performances in the finite impulse response section and provide the lowest power dissipation. The investigated adaptive filters overcome the state-of-the-art, exhibiting an area and power reduction with respect to the standard implementation up to −18.0% and −64.1%, respectively, while preserving the learning capabilities. [ABSTRACT FROM AUTHOR]

This paper presents static and dynamic theoretical analysis of a new Operational Transconductance Amplifier (OTA) dedicated to low-voltage and low-power applications. High linearity is obtained using signal attenuation for rail-to-rail operation. Design, layout and post-layout simulations have been carried-out with Cadence Virtuoso in a 0.18µm CMOS technology. Post-Layout Simulation (PLS) demonstrates proper operation down to ± 0.75 V supply voltage with a high open loop small-signal voltage gain of 58.26dB, a unity gain frequency of 18MHz and a total harmonic distortion of -66.55dB at 100 kHz for a peak-to-peak input voltage of 0.8V. Based on this circuit, two applications have been successfully demonstrated: on the one side, a 5th order Butterworth G mC filter and, on the other side, a voltage mode instrumentation amplifier. [ABSTRACT FROM AUTHOR]

Portable applications and battery-operated devices require highly reliable, stable, and low-power nanometer-sized embedded cache static random access memory (SRAM) cells. The conventional 6-transistor (6 T) SRAM cell and its variants suffer from malfunctioning during the read/write operations, and instability, and are vulnerable to the multi-bit soft-error rate at scaled technology node and low supply voltage (VDD). In this regard, this paper proposes an 12 T SRAM cell with reliable functioning and reduced multi-bit soft-errors appropriate for low-power portable applications. This cell performs single-end bitline decoupled read operation and write data-dependent feedback-cutting-aware differential write operation to improve the read static noise margin (RSNM) and write static noise margin (WSNM), respectively. The presence of stack transistors in the cell core and read path, and also high virtual ground (VGND) minimize the leakage power dissipation. The proposed cell is compared with other state-of-the-art SRAM cells at VDD = 0.7 V and under harsh process, voltage, and temperature (PVT) variations. It offers at least 1.18X higher RSNM, 1.27X higher WSNM, and 2.02X lower leakage power dissipation. It also shows the second-best read power and incurs a penalty in write power. This cell shows at least 1.17X, 1.32X, and 1.04X smaller spread in read delay, RSNM, and WSNM, respectively, when subjected to PVT variations. In addition, the proposed cell eliminates the write half-select disturbance by employing a separate gate to drive the access transistors and thus column-interleaving structure and error correction coding can be applied to reduce multiple-bit upset and increase soft-error immunity. The soft-error in the proposed cell is reduced by at least 1.37X in critical charge. Generally, the proposed cell offers the best overall performance among all the compared cells by showing the highest proposed figure of merit. [ABSTRACT FROM AUTHOR]

With the emergence of the internet-of-things (IoT), many devices process data through the IEEE 802.11 wireless networks. To satisfy the increased data processing, the wireless fidelity (Wi-Fi) networks must have sufficient capacity. IEEE 802.11ax is formulated to provide higher data processing capabilities for IoT applications. The IEEE 802.11ax receiver operating at 5 GHz must be able to withstand interference from nearby IoT applications. Hence, the low-noise amplifier (LNA) employed in such receivers must have a high input-referred third-order intercept point (IIP3). This paper aims to present a wideband LNA operating at 5 GHz with high IIP3 for modern IoT transceivers using IEEE 802.11ax protocol. The LNA is designed and implemented in UMC 65 nm CMOS technology using the Cadence virtuoso design tool. The proposed LNA can provide a maximum gain of 17.64 dB while consuming 8.18 mW of power from a 1.2 V supply. It can be noted that the minimum noise figure (NF) achieved is 3.67 dB and the corresponding IIP3 is 9.44 dBm at 5 GHz from the pre-layout simulations. [ABSTRACT FROM AUTHOR]

This paper presents a sub-microwatt sub-bandgap voltage and current reference that can generate proportional-to-absolute-temperature (PTAT) and complementary-to-absolute-temperature (CTAT) currents concurrently. The voltage reference is derived from the process-insensitive silicon bandgap voltage of a bipolar junction transistor, whereas the current reference is made by combining PTAT and CTAT currents. Line regulation is improved by incorporating cascode devices without an operational amplifier (opamp). Fabricated in a standard 0.18-µm CMOS process, the proposed bandgap reference occupies an active area of 0.4 mm2. The current and voltage reference ( I REF and V REF ) are measured as 170 mV and 21 nA, respectively, while the start-up settling response is measured as 20 ms at room temperature. The average temperature coefficient of I REF and V REF is 79.8 ppm/°C and 87.93 ppm/°C across the temperature range from − 40 to 120 °C, respectively. The power consumption is 134 nW at the minimum supply voltage of 1.2 V. The power supply ripple rejection of V REF is measured as − 10 dB at 100 kHz without any filtering capacitor, when the 1.6 V input line voltage is distorted by a 300-mVp-p ripple. The measured line sensitivity of the voltage and current reference is 0.142%/V and 0.757%/V, respectively. [ABSTRACT FROM AUTHOR]

In this paper, we analyze the performances of an Enhanced Static Segment Multiplier (ESSM) when the inputs have both uniform and non-uniform distribution. The enhanced segmentation divides the multiplicands into a lower, a middle, and an upper segment. While the middle segment is placed at the center of the inputs in other implementations, we seek the optimal position able to minimize the approximation error. To this aim, two design parameters are exploited: m, defining the size and the accuracy of the multiplier, and q, defining the position of the middle segment for further accuracy tuning. A hardware implementation is proposed for our generalized ESSM (gESSM), and an analytical model is described, able to find m and q which minimize the mean square approximation error. With uniform inputs, the error slightly improves by increasing q, whereas a large error decrease is observed by properly choosing q when the inputs are half-normal (with a NoEB up to 18.5 bits for a 16-bit multiplier). Implementation results in 28 nm CMOS technology are also satisfactory, with area and power reductions up to 71% and 83%. We report image and audio processing applications, showing that gESSM is a suitable candidate in applications with non-uniform inputs. [ABSTRACT FROM AUTHOR]

IoT and wearable medical devices frequently require ultra-low power solutions that can support long spells of inactivity. This study presents a buck converter to control power stages using a novel pulse frequency modulation (PFM) system that reduces switching losses for low-power systems. The modulation of high and low-frequencies was demonstrated, where the high-frequencies exhibited better energy transformation between the inductor and capacitor, and the low-frequencies could be adjusted for different current loads, to reduce switching losses. This circuit is optimized for light load applications. Using voltage control oscillation (VCO), the frequency range of 0.5 MHz – 2.0 MHz can be adjusted to influence conversion efficiency for different loads. The design was simulated and then fabricated using TSMC 0.18um process. The core size was about 1500 × 1000um that includes power MOS. Measurements result an average conversion efficiency of 91% under a load of 0.1 mA – 10 mA. This chip is suitable for battery-based IoT systems, or wearable medical devices. [ABSTRACT FROM AUTHOR]

A low-noise and -power readout integrated circuit (IC) for two-electrode electrocardiogram (ECG) recording is developed in this study using a common-mode charge pump (CMCP) for a robust 20-VPP common-mode interference (CMI). Two-electrode ECG recording offers more comfort than three-electrode ECG recording. Contrasting to the three-electrode ECG recording, the two-electrode ECG recording is affected by CMI during measurements; the intervention of a large CMI will distort the ECG signal measurement. To achieve robustness for the CMI, the proposed ECG readout IC adopts CMCP—it uses switched capacitors that store and subtract CMI by control logic. In this paper, a window comparator structure is applied to CMCP to obtain a signal with less distortion. The window voltage ranges were set between the input common-mode ranges in which IA can operate. Therefore, a signal with less distortion was obtained by stopping the operation of CMCP between the window voltage ranges. It also reduced additional current consumption. To achieve this, the proposed circuit is implemented using a chopper stabilization technique. The chopper implemented in the amplifier can reduce low-frequency noise components, such as 1/f noise, and it comprises a CMCP, current feedback instrumentation amplifier, QRS peak detector, relaxation oscillator, voltage reference, timing generator, and serial peripheral interface on a single chip. The proposed circuit was designed using a standard 0.18 μm CMOS process with an active area of 0.54 mm2. The proposed CMCP achieves a CMI robustness of 20 VPP at 60 Hz. The measured input-referred noise level was 119 nV/√Hz at 1 Hz, and the power consumption was 23.83 μW with a 1.8 V power supply. [ABSTRACT FROM AUTHOR]

This paper presents the first implementation of a spiking neural network (SNN) for the extraction of cepstral coefficients in structural health monitoring (SHM) applications and demonstrates the possibilities of neuromorphic computing in this field. In this regard, we show that spiking neural networks can be effectively used to extract cepstral coefficients as features of vibration signals of structures in their operational conditions. We demonstrate that the neural cepstral coefficients extracted by the network can be successfully used for anomaly detection. To address the power efficiency of sensor nodes, related to both processing and transmission, affecting the applicability of the proposed approach, we implement the algorithm on specialised neuromorphic hardware (Intel ® Loihi architecture) and benchmark the results using numerical and experimental data of degradation in the form of stiffness change of a single degree of freedom system excited by Gaussian white noise. The work is expected to open a new direction of SHM applications towards non-Von Neumann computing through a neuromorphic approach. [ABSTRACT FROM AUTHOR]

This paper proposes a new discrete cosine transform (DCT) processor. The micro-rotation section of the architecture is based on a shared-resource improved coordinate rotation digital computer (CORDIC) unit, in an enhanced scalable DCT engine. To reduce the resources, and utilization area all micro-rotation operations have implemented as one united block in overlapped form. Using one processing element, the memory-based architecture has reduced the power consumption. Inputs and outputs of the processor are in-order which can be taken into account as an advantage for the proposed design. The processor has a low-complexity and distributed controller. Furthermore, due to the shared-resource implementation of CORDIC-II unit, by reduction of adding, shifting operations both in size, and number, the processor has high capabilities in short word lengths in comparison with state-of-the-art DCT processors. Compared to existing prominent DCT processors, the proposed processor achieves better results with limited hardware resources. [ABSTRACT FROM AUTHOR]

A close correlation between CO2 concentration and aerosol enables the wide utilization of CO2 concentration as a good representation of Severe Acute Respiratory Syndrome-Coronavirus-2 infection airborne transmission. On the other side, many indoor air-quality monitoring devices have been developed for indoor monitoring applications. However, most of them are multiparameter air-quality sensor systems and tend to consume relatively high power, are relatively large devices, and are fairly expensive; therefore, they not meet the requirement for indoor monitoring applications. This paper presents a smart wireless sensor node that can measure and monitor CO2 concentration levels. The node was designed to meet the requirements of indoor air-quality monitoring applications by considering several factors, such as compact size, low cost, and low power, as well as providing real-time, continuous, reliable, and remote measurement. Furthermore, the commercial off-the-shelf and low-power consumption components are chosen to fit with the low-cost development and reduce energy consumption. Moreover, a low-power algorithm and cloud-based data logger also were applied to minimize the total power consumption. This power strategy was applied as a preliminary development toward an autonomous sensor node. The node has a compact size and consumes low energy for one cycle of CO2 measurement, accompanied by high accuracy with very low measurement error. The experiment result revealed the node could measure and monitor in real-time continuous, reliable, and remote CO2 concentration levels in indoor and outdoor environments. A user interface visualizes CO2 concentration graphically and numerically using the Adafruit platform for easy accessibility over the Internet of Things. The developed node is very promising and suitable for indoor CO2 monitoring applications with the acquired data that could be utilized as an indicator to minimize the risk of indoor Severe Acute Respiratory Syndrome-Coronavirus-2 airborne transmission. [ABSTRACT FROM AUTHOR]

This paper investigates a novel highly stable and robust single-ended 10T SRAM cell appropriate for low-power portable applications. The cell core of the proposed design is a combination of a normal inverter with a stacked NMOS transistor and a Schmitt-trigger (ST) inverter with a double-length pull-up transistor. This improves hold stability and leakage power dissipation. The read and write operations of the proposed cell are performed with the aid of separated paths and bitlines, lowering power consumption. The strong cell core and decoupled read path eliminate the read-disturbance issue in the proposed cell, resulting in read static noise margin (RSNM) enhancement. Furthermore, the feedback-cutting write-assist technique used in the proposed design mitigates the writing '1' issue; consequently, write static noise margin (WSNM)/write margin (WM) improves. To prove the superiority of the proposed SRAM cell in various performance metrics, it is compared with state-of-the-art SRAM cells, introduced as 6T, TG9T, 10T-P1, and SB11T, using HSPICE and 16-nm CMOS technology node taking into consideration the impact of the severe process, voltage, and temperature (PVT) variations. Obtained results at VDD = 0.7 V show that the proposed design offers the highest HSNM/RSNM/WSNM (or WM). The read/write delay of the proposed cell is 3.92X/2.37X higher than that of the 6T SRAM cell due to its single-ended reading/writing structure. However, in terms of power consumption, the proposed cell exhibits 1.64X/1.54X lower than that of 6T SRAM cell. Though the proposed cell occupies a 1.24X higher area compared with the 6T SRAM cell due to its higher count of transistor, it shows the highest proposed figure of merit among all the studied SRAM cells, which is 26.90X higher than that of 6T SRAM cell. [ABSTRACT FROM AUTHOR]

One of the most significant agricultural tasks in beekeeping involves continually observing the conditions inside and outside the beehive. This is mainly performed for the early detection of some harmful events. There have been many studies on how to detect and prevent such occurrences by performing periodic interventions or, when the frequency of such actions is hard to enforce, by using sensory systems that record the temperature, humidity, and weight of the beehive. Nevertheless, such methods are inaccurate, and their delivered outcomes usually diverge from the actual event or false trigger and introduce more effort and damage. In this paper, the authors propose a new low-cost, low-power system called Bee Sound Detector (BeeSD). BeeSD is a low-cost, embedded solution for beehive quality control. It incorporates the sensors mentioned above as well as real-time sound monitoring. With the combination of temperature, humidity, and sound sensors, the BeeSD can spot Colony Collapse Disorder events due to famine and extreme weather events, queen loss, and swarming. Furthermore, as a system, the BeeSD uses cloud logging and an appropriate mobile phone application to push notifications of extreme measurements to the farmers. Based on achieved performance indicators, the authors present their BeeSD IoT device and system operation, focusing on its advantages of low-cost, low-power, and easy-to-install characteristics. [ABSTRACT FROM AUTHOR]

In this paper, a small-area low-power 64-bit integer multiplier is presented, which is suitable for portable devices or wireless applications. To save the area cost and power consumption, an input vector systolic (IVS) structure is proposed based on four 16-bit radix-8 Booth multipliers and a data input scheme is proposed to reduce the number of signal transitions. This structure is similar to a systolic array in matrix multiply units of a Convolutional Neural Network (CNN), but it reduces the number of processing elements by 3/4 concerning the same vector systolic accelerator in reference. The comparison results prove that the IVS multiplier reduces at least 61.9% of the area and 45.18% of the power over its counterparts. To increase the hardware resource utilization, a Transverse Carry Array (TCA) structure for Partial Products Accumulation (PPA) was designed by replacing the 32-bit adders with 3/17-bit adders in the 16-bit multipliers. The experiment results show that the optimization could lead to at least a 6.32% and 13.65% reduction in power consumption and area cost, respectively, compared to the standard 16-bit radix-8 Booth multiplier. In the end, the precise scale of the proposed IVS multiplier is discussed. Benefiting from the modular design, the IVS multiplier can be configured to support sixteen different kinds of multiplications at a step of 16 bits [16b, 32b, 48b, 64b] × [16b, 32b, 48b, 64b]. [ABSTRACT FROM AUTHOR]

In this paper, a new high-gain operational transconductance amplifier (OTA) circuit is designed. The proposed circuit uses bulk-driven technique to reduce the required supply voltage while gain-boosting as well as positive feedback techniques are used to increase the gain of the circuit and compensate the gain reduction effect due to the use of bulk-driven transistors. The performance of the proposed circuit is simulated using 180-nm CMOS technology parameters and is optimized using an optimization algorithm for the main purpose of minimizing the power consumption of the circuit. Simulated performance of the proposed circuit for 1.2 V DC supply voltage shows 94.8 dB gain, 203-MHz bandwidth, and slew-rate of 365-V/µS while occupying die area of 1360 µm2. However, the proposed circuit is also designed for a low-voltage scenario with 0.75 V DC supply voltage that results in 73.7 dB gain, 131-MHz bandwidth, and slew-rate of 149-V/µS. [ABSTRACT FROM AUTHOR]

Sensitivity is a key parameter for microspeakers, especially in low-power applications such as consumer electronics, medical devices and Internet of Things. However, the sensitivities of most reported piezoelectric MEMS speakers are inferior to those of commercially available electrodynamic speakers. Design of high-sensitivity piezoelectric MEMS speakers requires an analytical expression of sensitivity, which is not yet available. In this paper, the analytical expression is derived, and an ultrahigh-sensitivity piezoelectric MEMS speaker is designed, fabricated and characterized. The sensitivities of the speaker in an ear simulator are 107 dB/mW, 104 dB/mW and 105 dB/mW at 250 Hz, 1 kHz and 10 kHz, respectively, which are the highest among piezoelectric MEMS speakers. The analytical calculation results and measurement data are perfectly matched. Furthermore, the speaker is based on thin-film AlN instead of commonly used thin-film PZT. This work demonstrates that the ultrahigh-sensitivity piezoelectric AlN MEMS speaker consumes 10 times less power than a typical 6 mm electrodynamic speaker; and the developed analytical expressions provide valuable design insights into piezoelectric MEMS speakers for high sensitivity and low power. [2022-0063] [ABSTRACT FROM AUTHOR]

In this paper, a latch‐based energy‐efficient dynamic comparator is presented in Carbon Nanotube Field Effect Transistor (CNTFET) technology. The proposed comparator consists of two main stages: pre‐amplifier and latch. The latch stage is designed for the main purpose of low‐power consumption and high‐speed performances. The proposed speed‐up technique for the latch structure controls the threshold voltage (Vth) of the cross‐coupled inverters. So, the delay of the latch stage decreases and consequently, the overall delay of the comparator circuit is also reduced up to 19.4% while the maximum speed performance of the proposed comparator increases by 54% compared to the conventional double‐tail dynamic comparator. Additionally, the use of the proposed distinctive structure for the tail transistors in the latch stage, leads to more than 11% reduction in the energy per conversion of the proposed circuit compared to the conventional double‐tail dynamic comparator. To verify the circuit performances, the comparator circuit is simulated in HSPICE using 32 nm CNTFET Stanford model technology parameters. The simulation results show that the proposed comparator with the proposed speed‐up approach can operate up to 14.2 GHz with a sensitivity of 30 μV at the supply voltage of 1 V, while consumes only 42.38 μW of power. Therefore, the proposed comparator can be used in high‐resolution (up to 12 bit) and high‐speed low‐power analogue‐to‐digital converter applications. Moreover, the effects of the non‐ideal fabrication process (including the pitch and the threshold voltage variations), supply voltage and temperature variations are investigated in this work. Monte‐Carlo analysis shows that the standard deviation of the offset voltage is approximately 1.24 mV. Finally, the kickback noise of the proposed comparator is obtained as 80 μV, which shows the proper performance of the proposed comparator circuit in comparison with other reported designs. [ABSTRACT FROM AUTHOR]

This paper presents the design and analysis of a new low-voltage and low-power OTA-C universal filter which can realise four different system topologies including: Single-Input Single-Output (SISO), Single-Input Multi-Output (SIMO), Multi-Input Single-Output (MISO) and Multi-Input Multi-Output (MIMO), without any requirement of extra switching or adding other active elements to the core filter circuit. In addition, the proposed circuit has the ability to operate in all modes of operation (multi-mode) using the grounded capacitors to effectively reduce the parasitic effects. Furthermore, to decrease the power consumption effectively, the proposed OTA-C filter is biased in the subthreshold region which significantly reduces the power consumption. Moreover, the proposed circuit has a very low sensitivity to the values of transconductance and capacitance elements. Finally, the performance of the proposed universal filter is simulated in HSPICE, using 0.18 µm CMOS technology parameters. However, the simulation results justify the design accuracy and low-power performance as well as the capability of generating filtering responses simultaneously in the different system configurations. [ABSTRACT FROM AUTHOR]

Most image processing applications are naturally imprecise and can tolerate computational error up to a specific limit. In such applications, savings in power are achieved by pruning the data path units, such as an adder module. Truncation, however, may lead to errors in computing, and therefore, it is always a challenge between the amount of error that can be tolerated in an application and savings achieved in area, power and delay. This paper proposes a segmented approximate adder to reduce the computation complexity in error-resilient image processing applications. The sub-carry generator aids in achieving a faster design while carry speculation method employed improves the accuracy. Synthesis results indicate a reduced die-area up to 36.6%, improvement in delay up to 62.9%, and reduction in power consumption up to 34.1% compared to similar work published previously. Finally, the proposed adder is evaluated by using image smoothing and sharpening techniques. Simulations carried out on these applications prove that the proposed adder obtains better peak signal-to-noise ratio than those available in the literature. [ABSTRACT FROM AUTHOR]

The use of RISC-based embedded processors aimed at low cost and low power is becoming an increasingly popular ecosystem for both hardware and software development. High-performance yet low-power embedded processors may be attained via the use of hardware acceleration and Instruction Set Architecture (ISA) extension. Recent publications of AI have demonstrated the use of Coordinate Rotation Digital Computer (CORDIC) as a dedicated low-power solution for solving nonlinear equations applied to Neural Networks (NN). This paper proposes ISA extension to support floating-point CORDIC, providing efficient hardware acceleration for mathematical functions. A new DMA-based ISA extension approach integrated with a pipeline CORDIC accelerator is proposed. The CORDIC ISA extension is directly interfaced with a standard processor data path, allowing efficient implementation of new trigonometric ALU-based custom instructions. The proposed DMA-based CORDIC accelerator can also be used to perform repeated array calculations, offering a significant speedup over software implementations. The proposed accelerator is evaluated on Intel Cyclone-IV FPGA as an extension to Nios processor. Experimental results show a significant speedup of over three orders of magnitude compared with software implementation, while applied to trigonometric arrays, and outperforms the existing commercial CORDIC hardware accelerator. [ABSTRACT FROM AUTHOR]

Achieving high-gain and low-noise operation of bulk-driven (BD) operational transconductance amplifier (OTA) with low-power consumption is a challenging task owing to inherent low transconductance and high noise in BD MOSFET. In this paper, we report a self-cascode partial positive feedback (SCPPF) approach-based BD OTA and have fairly compared the results with regular single MOS partial positive feedback (PPF) approach-based BD OTA. In comparison to regular BD PPF OTA, the proposed BD SCPPF OTA yields an improved gain of 68.71 dB, gain-bandwidth of 39.4 kHz, power consumption of 630 nW, output impedance of 0.4 MΩ and common mode rejection ratio of 134.4 dB for same input referred noise of 1.11 μV/√ Hz at frequency of 10 Hz and supply voltage of ±0.5 V. The area consumed by BD SCPPF OTA is 0.0523 mm2. The proposed BD SCPPF OTA can be possibly used to improve the performance of biomedical analog front-end sensing and signal processing systems. [ABSTRACT FROM AUTHOR]

Content addressable memory (CAM) is a hardware search engine (HSE) used for accelerating lookup functions in tagged cache, translation lookaside buffers (TLBs) and network routers. Despite its speedy search feature, augmentation of compare-evaluate blocks to storage units costs additional area. Activation of cells and matchlines (MLs) during search operations is responsible for huge dynamic-power dissipation. This paper proposes a shared ML scheme (SMS) based on a two-step evaluation CAM cell, in order to reduce ML transitions per word besides improving density in the comparison units. A 64 × 32-bit proposed SMS-CAM, using 45-nm CMOS technology, dissipates 1.06 fJ/bit/search and achieves 100.77-ps search time. At the cost of 6.55% sacrificial delay over a conventional CAM, the proposed CAM reduces 12.38% – 30.48% energy over a self-power off gating, the conventional and an early terminate ML precharge designs. Low energy-delay results better trade-off budget besides decreasing macro area in the SMS-CAM, which could be useful in search applications of network devices. • A 2-bit storage with common evaluation (2B-CE) involving two-step comparison • Two side-by-side CAM cells (one 2B-CE based cell) share one matchline (ML) node • Shared matchline scheme limits ML discharge to only 1 path per 2-bit mismatches • Lesser ML discharge paths with shared matchline scheme (SMS) saves dynamic power • N/2 proposed 2B-CE cells are designed in NOR-type ML to form an N-bit CAM word • SMS-CAM sustains high-speed searches owing to the parallel cells in NOR-type ML • Comparison of SMS-CAM with referred CAMs verifies performance and reliability [ABSTRACT FROM AUTHOR]

Radio frequency energy harvesting (RFEH) and wireless power transmission (WPT) are two emerging alternative energy technologies that have the potential to offer wireless energy delivery in the future. One of the key components of RFEH or WPT system is the receiving antenna. The receiving antenna’s performance has a considerable impact on the power delivery capability of an RFEH or WPT system. This paper provides a well-rounded review of recent advancements of receiving antennas for RFEH and WPT. Antennas discussed in this paper are categorized as low-profile antennas, multi-band antennas, circularly polarized antennas, and array antennas. A number of contemporary antennas from each category are presented, compared, and discussed with particular emphasis on design approach and performance. Current design and fabrication challenges, future development, open research issues of the antennas and visions for RFEH and WPT are also discussed in this review.

This paper reports on a novel solution for a transimpedance amplifier (TIA) specifically designed as an analog conditioning circuit for low-voltage, low-power, wearable, portable and implantable optoelectronic integrated sensor systems in biomedical applications. The growing use of sensors in all fields of industry, biomedicine, agriculture, environment analysis, workplace security and safety, needs the development of small sensors with a reduced number of electronic components to be easily integrated in the standard CMOS technology. Especially in biomedicine applications, reduced size sensor systems with small power consumption are of paramount importance to make them non-invasive, comfortable tools for patients to be continuously monitored even with personalized therapeutics and/or that can find autonomous level of life using prosthetics. The proposed new TIA architecture has been designed at transistor level in TSMC 0.18 μm standard CMOS technology with the aim to operate with nanoampere input pulsed currents that can be generated, for example, by Si photodiodes in optical sensor systems. The designed solution operates at 1.8 V single supply voltage with a maximum power consumption of about 36.1 μW and provides a high variable gain up to about 124 dBΩ (with fine- and coarse-tuning capabilities) showing wide bandwidth up to about 1.15 MHz and low-noise characteristics with a minimum noise floor level down to about 0.39 pA/ Hz . The overall circuit is described in detail, and its main characteristics and performances have been analyzed by performing accurate post-layout simulations. [ABSTRACT FROM AUTHOR]

【Objective】In this paper, an ultra-low power consumption optical transmitter is developed to broaden the application of optical communication in short-reach interconnection such as board level and backplane level. The basic principle of the scheme is that the Vertical-Cavity Surface-Emitting Laser (VCSEL) laser generates an optical pulse signal after loading a bias current and a modulation signal through a Bias-T circuit.【Methods】In this work, the feasibility of the scheme is verified by means of simulation. It is also found that the main factors affecting the increase of the transmission rate are the parasitic parameters of the laser device and the impedance matching of the drive circuit. The two electrical transmission distances of 200 and 10 mm are studied by experiments. And the law of eye diagram quality and bit error rate as a function of driving voltage and bias current is studied through experiments under two electrical transmission distances.【Results】The results show that a larger effective extinction ratio ranging from 0.84 to 6.69 dB can be obtained by shortening the electrical transmission distance to 10 mm with the minimum differential driving voltage of 200 mV, the minimum bias current of 15 mA, and the power consumption of 1.2 mW/Gbit/s. Compared with traditional optical modules, the power consumption of optical transmission is reduced by about 80%.【Conclusion】This optical transmitter solution can be applied to intra-data center. Combined with low-power optical receivers, the overall power consumption of the data center can be significantly reduced.

The survival rate for sudden cardiac arrest (SCA) is low, and patients with long-term risks of SCA are not adequately alerted. Understanding SCA’s characteristics will be key to developing preventive strategies. Many lives could be saved if SCA’s early onset could be detected or predicted. Monitoring heart signals continuously is essential for diagnosing sporadic cardiac dysfunction. An electrocardiogram (ECG) can be used to continuously monitor heart function without having to go to the hospital. A zeolite-based dry electrode can provide safe on-skin ECG acquisition while the subject is out-of-hospital and facilitate long-term monitoring. To the ECG signal, a low-power 1 μW read-out circuit was designed and implemented in our prior work. However, having long-term ECG monitoring outside the hospital, i.e., high battery life, and low power consumption while transmission and reception of ECG signal are crucial. This paper proposes a prototype with a 10-bit resolution ADC and nRF24L01 transceivers placed 5 m apart. The system uses the 2.4 GHz worldwide ISM frequency band with GFSK modulation to wirelessly transmit digitized ECG bits at 250 kbps data rate to a physician’s computer (or similar) for continuous monitoring of ECG signals; the power consumption is only 11.2 mW and 4.62 mW during transmission and reception, respectively, with a low bit error rate of ≤0.1%. Additionally, a subject-wise cross-validated, three-fold, optimized convolutional neural network (CNN) model using the Physionet-SCA dataset was implemented on NVIDIA Jetson to identify the irregular heartbeats yielding an accuracy of 89% with a run time of 5.31 s. Normal beat classification has an F1 score of 0.94 and a ROC score of 0.886. Thus, this paper integrates the ECG acquisition and processing unit with low-power wireless transmission and CNN model to detect irregular heartbeats.

In this paper, a new low-power full-adder circuit based on the proper combination of dynamic logic style and Gate Diffusion Input (GDI) low-power technique is proposed in Carbon Nanotube Field Effect Transistor (CNFET) technology. Using the proposed approach, the basic logic circuits such as XOR and XNOR gates are implemented which results in a full-swing, full-adder cell in the CNFET technology. The proposed circuit is simulated in HSPICE using CNFET model parameters. Finally, the simulation results justify a good improvement in the major circuit performances such as power consumption, delay and power-delay product (PDP) parameters for the proposed full-adder circuit. • A new Dynamic-GDI Low-Power Full-Adder circuit in CNFET technology is presented. • The design is based on the proper combination of Dynamic and GDI logic design techniques. • The proposed design not only significantly reduces the power consumption, but also reduces the PDP and layout area considerably. • The performance of the proposed circuit is simulated in HSPICE, using the STANFORD CNFET model parameters. [ABSTRACT FROM AUTHOR]

This article presents the design of a low-power low noise amplifier (LNA) implemented in 45 nm silicon-on-insulator (SOI) technology using the g m / I D methodology. The Ka-band LNA achieves a very low power consumption of only 1.98 mW andis the first time the g m / I D approach is applied at such a high frequency. The circuit is suitable for Ka-band applications with a central frequency of 28 GHz, as the circuit is intended to operate in the n257 frequency band defined by the 3GPP 5G new radio (NR) specification. The proposed cascode LNA uses the g m / I D methodology in an RF/MW scenario to exploit the advantages of moderate inversion region operation. The circuit occupies a total area of 1.23 mm2 excluding pads and draws 1.98 mW from a DC supply of 0.9 V. Post-layout simulation results reveal a total gain of 11.4 dB, a noise figure (NF) of 3.8 dB, and an input return loss (IRL) better than 12 dB. Compared to conventional circuits, this design obtains a remarkable figure of merit (FoM) as the LNA reports a gain and NF in line with other approaches with very low power consumption. [ABSTRACT FROM AUTHOR]

As machine learning and AI continue to rapidly develop, and with the ever-closer end of Moore’s law, new avenues and novel ideas in architecture design are being created and utilized. One avenue is accelerating AI as close to the user as possible, i.e., at the edge, to reduce latency and increase performance. Therefore, researchers have developed low-power AI accelerators, designed specifically to accelerate machine learning and AI at edge devices. In this paper, we present an overview of low-power AI accelerators between 2019–2022. Low-power AI accelerators are defined in this paper based on their acceleration target and power consumption. In this survey, 79 low-power AI accelerators are presented and discussed. The reviewed accelerators are discussed based on five criteria: (i) power, performance, and power efficiency, (ii) acceleration targets, (iii) arithmetic precision, (iv) neuromorphic accelerators, and (v) industry vs. academic accelerators. CNNs and DNNs are the most popular accelerator targets, while Transformers and SNNs are on the rise.

Peripheral nerve injuries pose significant health challenges leading to loss of motor control and sensation. These injuries can result from trauma, surgery, tumors, and neurological disorders. Peripheral nerve injuries mainly lead to paralysis such as facial paralysis. Patients with facial paralysis often face loss of facial expression control affecting their quality of life. Research studies have explored peripheral nerve recording to address these challenges, aiming to understand nerve impulses and develop effective neural interfaces for functional restoration. Neural amplifier chips characterized by low noise, high gain, and small size have been integrated with nerve cuff electrodes to capture neural signals efficiently. This paper reviews the applications of cuff electrodes simulating the facial nerve for facial muscle contractions and various neural recording system architectures interfaced with a peripheral nerve cuff electrode. Diverse types of cuff electrodes and their configuration have been drawn pointing out their advantages and limitations. Neural amplifier topologies susceptible to neural recording are outlined with their main characteristics including noise, speed, and power consumption relative to their general architectures.

This paper presents a static sequential-random-access memory (S2RAM) designed to enable low-power and high-performance operations for workloads involving sequential memory accesses. Considering the address configuration and internal operation of column-interleaved SRAM, we propose an optimized memory structure and peripheral circuitry that can exploit access patterns as opportunities for low-cost operation. In the proposed S2RAM, word-line (WL) activation is restricted by using bit-line (BL) as temporary storage in case of memory access in which only column addresses are sequentially changed. In order to prevent the BL leakage-induced data corruption, a BL clamper is also proposed. The optimized design method of the controller and assist scheme for using reconfigurable access modes in the proposed S2RAM is also presented. The proposed 16Kb S2RAM macro has been implemented using a 28nm CMOS technology. Numerical results show that the proposed S2RAM saves up to 76% of operating energy compared to conventional SRAM at the cost of 8% increased area.