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The electrocardiogram (ECG) is one of the most extensively employed signals used to diagnose and predict cardiovascular diseases (CVDs). In recent years, several deep learning (DL) models have been proposed to improve detection accuracy. Among these, deep neural networks (DNNs) are the most popular, wherein the features are extracted automatically. Despite the increment in classification accuracy, DL models require exorbitant computational resources and power. This causes the mapping of DNNs to be slow; in addition, the mapping is challenging for a wearable device. Embedded systems have constrained power and memory resources. Therefore full-precision DNNs are not easily deployable on devices. To make the neural network faster and more power-efficient, spiking neural networks (SNNs) have been introduced for fewer operations and less complex hardware resources. However, the conventional SNN has low accuracy and high computational cost. Therefore, this paper proposes a new binarized SNN which modifies the synaptic weights of SNN constraining it to be binary (+1 and -1). In the simulation results, this paper compares the DL models and SNNs and evaluates which model is optimal for ECG classification. Although there is a slight compromise in accuracy, the latter proves to be energy-efficient.

Image processing is one of the major techniques that are used for computer vision. Nowadays, researchers are using machine learning and deep learning for the aforementioned task. In recent years, digit recognition tasks, i.e., automatic meter recognition approach using electric or water meters, have been studied several times. However, two major issues arise when we talk about previous studies: first, the use of the deep learning technique, which includes a large number of parameters that increase the computational cost and consume more power; and second, recent studies are limited to the detection of digits and not storing or providing detected digits to a database or mobile applications. This paper proposes a system that can detect the digital number of meter readings using a lightweight deep neural network (DNN) for low power consumption and send those digits to an Android mobile application in real-time to store them and make life easy. The proposed lightweight DNN is computationally inexpensive and exhibits accuracy similar to those of conventional DNNs.

In this paper, a negative high voltage DC-DC converter using a new cross-coupled charge pump structure has been proposed, which can solve the shoot-through current problem of the conventional charge pump by using a four clock phase scheme. Also, by switching the power supply to each stage based on the supply voltage, a variable voltage gain can be obtained. A complete analysis of the interaction between the power efficiency, area, and frequency have been presented. The proposed negative charge pump is designed to deliver 40μA with a widesupply range from 2.5V to 5.5V using 0.18μm high voltage LDMOS technology.

This paper proposes a novel Built-in Self Calibration (BISC) technique for a 12-bit 32MS/s successive approximation register (SAR) analog-to-digital converter (ADC) using a single input to reduce the capacitor mismatch of the digital-to-analog converter (DAC) and to compensate the comparator input offset voltage. The proposed self-calibration scheme optimize the mismatch of the DAC by changing additional auxiliary capacitor array during calibration mode. In addition, in order to minimize the offset voltage of the comparator in the SAR ADC, a simplified voltage amplifier is proposed. The controller for the proposed algorithm operates as foreground operation to achieve low power consumption during operation. Compared to the converters that use the conventional procedure, INL and DNL are reduced by about 47% and 52%, respectively. The prototype was designed using 130nm single poly 6 metal standard CMOS technology. The ADC achieves a SNDR of 65.6 dB and consumes 4.62 mW. The ADC core occupies an active area of only 240μmÍ 298 μm using 1.2V supply and the sampling rate of 50 MS/s.

Electrocardiogram (ECG) classification has become an essential task of modern day wearable devices, and can be used to detect cardiovascular diseases. State-of-the-art Artificial Intelligence (AI)-based ECG classifiers have been designed using various artificial neural networks (ANNs). Despite their high accuracy, ANNs require significant computational resources and power. Herein, three different ANNs have been compared: multilayer perceptron (MLP), convolutional neural network (CNN), and spiking neural network (SNN) only for the ECG classification. The ANN model has been developed in Python and Theano, trained on a central processing unit (CPU) platform, and deployed on a PYNQ-Z2 FPGA board to validate the model using a Jupyter notebook. Meanwhile, the hardware accelerator is designed with Overlay, which is a hardware library on PYNQ. For classification, the MIT-BIH dataset obtained from the Physionet library is used. The resulting ANN system can accurately classify four ECG types: normal, atrial premature contraction, left bundle branch block, and premature ventricular contraction. The performance of the ECG classifier models is evaluated based on accuracy and power. Among the three AI algorithms, the SNN requires the lowest power consumption of 0.226 W on-chip, followed by MLP (1.677 W), and CNN (2.266 W). However, the highest accuracy is achieved by the CNN (95%), followed by MLP (76%) and SNN (90%).

Most of the existing hand detection methods rely on the contour shape of hand after skin color segmentation. Such contour shape based computations, however, are not only susceptible to noise and other skin color segments but also inherently sequential and difficult to efficiently parallelize. In this paper, we implement and accelerate our in-house distance image based approach using CPU-GPU heterogeneous computing. Using emerging CPU-GPU heterogeneous computing technology, we achieved 5.0 times speed-up for 320x240 images, and 17.5 times for 640x480 images and our experiment demonstrates that our proposed distance image based hand detection is robust and fast, reaching up to 97.32% palm detection rate, 80.4% of which have more than 3 fingers detected on commodity processors.

In this paper, a 12-bit high resolution, power and area efficiency hybrid digital pulse width modulator (DPWM) with process and temperature (PT) calibration has been proposed for digital controlled DC-DC converters. The hybrid structure of DPWM combines a 6-bit differential tapped delay line ring-mux digital-to-time converter (DTC) schema and a 6-bit counter-comparator DTC schema, resulting in a power and area saving solution. Furthermore, since the 6-bit differential delay line ring oscillator serves as the clock to the high 6-bit counter-comparator DTC, a high frequency clock is eliminated, and the power is significantly saved. In order to have a simple delay cell and flexible delay time controllability, a voltage controlled inverter is adopted to build the deferential delay cell, which allows fine-tuning of the delay time. The PT calibration circuit is composed of process and temperature monitors, two 2-bit flash ADCs and a lookup table. The monitor circuits sense the PT (Process and Temperature) variations, and the flash ADC converts the data into a digital code. The complete circuits design has been verified under different corners of CMOS 0.18um process technology node.

Conventional synchronous design circuits cannot only satisfy the timing requirement of the low voltage digital systems, but also they may generate wrong outputs under the influence of PVT variations and aging effects. Therefore, in this paper, a NCL (Null Convention Logic) design as an asynchronous design method has been proposed, where the NCL method doesn`t require any timing analysis, and it has a very simple design methodology. Base on the NCL method, a new low power reliable ALU has been designed and implemented using MagnaChip-SKhynix 0.18um CMOS technology. The experimental results of the proposed NCL ALU have been compared to those of a conventional pipelined ALU in terms of power consumption and speed.

As CMOS technology is scaled down more aggressively, the reliability mechanism (or aging effect) caused by the diffusion of metal atoms along the conductor in the direction of the electron flow, also called electromigration (EM), has become a major reliability concern. With the present of EM, it is difficult to control the current flows of the MOSFET device and interconnect. In addition, nanoscale CMOS circuits suffer from increased gate leakage current and power consumption. In this paper, the EM effects on current of the nanoscale CMOS circuits are analyzed. Finally, this paper introduces an on-chip current measurement method providing lifetime electromigration management which are designed using 45-nm CMOS predictive technology model.

This paper proposes presents a low power and high-speed four-quadrant analog multiplier in the current mode based on dual translinear loops using 32nm CMOS and 32nm CNTFET technologies to investigate and compare the performance differences of the analog circuits on CNTFET technology and CMOS 32nm technology nodes. All the simulations were performed using hspice with 32nm CMOS from PTM library and 32nm CNTFET from Stanford University technologies at the same power supply level. CNTFET based multiplier shows a wider linearity over considerable range of outputs (−10 μA to +10μA) while the CMOS based multiplier shows (−7μA to +7μA) and the 3db frequency of the CNTFET based multiplier is 110GHz while the 3dB frequency of the CMOS based multiplier is only 2.45GHz.

Any real architecture designed only in the CNFET technology as a hopeful substitution of the silicon CMOSFET has not been developed because of shortage of self-assembly CNFET technology for designing complex CNFET structures. Therefore, for designing the real architecture in the current self-assembly CNFET technology, the development of a simple CNFET circuit structure forming all the digital function is required. This paper proposes a simple CNFET circuit structure using back-gate voltages to design the real digital architecture and to overcome the high fabrication cost of CNFETs and manufacturing variability and imperfection of CNFET technology. The function of the proposed CNFET cell is determined by the back-gate voltages, and the determined function is the same as NAND or NOR gate function. The simulation results present that the propagation delay time of the ISCAS85 circuits in a 32nm Stanford CNFET technology deploying the proposed CNFET cells is reduced by over 42% compared to the conventional CNFET cell in ultra-low voltage (0.4V).