Total System Power Consumption Research Articles

A 1024-Channel Simultaneous Electrophysiological and Electrochemical Neural Recording System with In-Pixel Digitization and Crosstalk Compensation.

Simultaneous electrophysiological and chemical recording allows for multi-modal neural instrumentation and provides insights into chemical synapses and ion channels across the cell membrane. However, intermodal interference can hinder highly synchronized recording in large-scale systems with high temporal and spatial resolution. In this work, we propose a 1024-channel lab-on-CMOS system for dual-modal neural recording with in-pixel digitization and interference suppression. A foreground calibration scheme with tunable capacitance is implemented in-pixel to compensate for the crosstalk between electrical and chemical recording. Active pixels for both electrical and chemical modalities are designed based on a pulse width modulation (PWM) analog-to-digital conversion scheme. CMOS-compatible post-processing is implemented to realize in-pixel electrodes and chemical sensing membranes. The prototype, implemented in a 180nm CMOS technology, occupies a total area of 33mm2 with 1024 pixels, and each unit pixel includes one electrical recording site and two chemical recording sites, with dimensions of 150μm×130 μm. The total system power consumption is 19.68mW at a frame rate of 9k and 3k for electrical and chemical imaging respectively. The in-vitro experiment demonstrated the concurrent high density electrophysilogical and electrochemical recording with sub millisecond temporal resolution.

Hardware-Accelerated YOLOv5 Based on MPSoC

This paper details the development of a hardware acceleration system for YOLOv5, focusing on flame detection as its primary application. The implementation leverages the APU and DPU functionalities integrated into the Zynq UltraScale+ MPSoC XCZU7EV core. The proposed solution addresses the challenge of achieving real-time target detection on mobile terminals, ensuring both real-time operation and ultralow power consumption of YOLOv5. Notably, our design approach facilitates the deployment of all target detection algorithms under TensorFlow for mobile devices. To optimize model efficiency, we employ saturated linear mapping quantization with calibration. This technique maps model weights, double bases, and activations from 32-bit to 8-bit, incurring only a 1.64% accuracy loss. The data flow design is realized through efficient data exchange between DDR, APU, and DPU, utilizing the AXI4 bus architecture. Image pre-processing and post-processing tasks are executed on the APU, while neural network inference occurs on the DPU. Our accelerated system demonstrates compelling experimental results: maintaining a detection speed of 56FPS, achieving an accuracy of 36.56% on the COCO2014 dataset, and exhibiting a total system power consumption of only 4.147W. Furthermore, the energy consumption ratio is measured at 15.41GOPS/W, surpassing the RTX A6000 graphics card by a factor of 55.

Joint Precoding for Active Intelligent Transmitting Surface Empowered Outdoor-to-Indoor Communication in mmWave Cellular Networks

Outdoor-to-indoor communications in millimeter-wave (mmWave) cellular networks have been one challenging research problem due to the severe attenuation and the high penetration loss caused by propagation characteristics of mmWave signals. We propose a viable solution to implement the outdoor-to-indoor mmWave communication with the aid of an active intelligent transmitting surface (active-ITS), where the active-ITS allows the incoming signal from an outdoor base station (BS) to pass through the surface and be received by indoor users (UEs) after shifting its phase and magnifying its amplitude. Then, the problem of joint precoding of the BS and active-ITS is investigated to maximize the weighted sum-rate (WSR) of the system. An efficient block coordinate descent (BCD) based algorithm is developed to solve it with the suboptimal solutions in nearly closed-forms. In addition, to reduce the size and hardware cost of active-ITSs, we provide a block-amplifying architecture to partially remove the circuit components for power-amplifying, where multiple transmissive-type elements (TEs) in each block share the same power amplifier. Simulations indicate that active-ITS has the potential of achieving a given performance with much fewer TEs compared to the passive-ITS under the same total system power consumption, which makes it suitable for application to the space-limited and aesthetic-needed scenario, and the performance degradation caused by the block-amplifying architecture is negligible.

Humidity grade-matched fresh air dehumidification system with adjustable treating stages

Air conditioning systems using liquid desiccants are efficient in dehumidification, but their performance can be further improved under both design and different partial load conditions. In this study, a humidity grade-matched system with adjustable treatment stages is designed based on the use of appropriate humidity sinks to handle loads. The method to determine reasonable treatment stage numbers and packing tower areas is given by optimizing the total system energy consumption and economic performance under different working conditions, based on validated numerical system performance models and device prices from the market or literature. The performance of the proposed system is compared with traditional systems in a case office building in Nanjing. The results are as follows: 1. By optimizing the total system power consumption and economic performance under different conditions, the humidity difference for one-stage and mass transfer humidity difference should be set at 3–5 and 2–3 g/kg to determine the stage number and packing tower area, respectively. 2. The proposed fresh air dehumidification system as applied to an office building without exhaust air in Nanjing reduces 16.6% and 13.5% of the seasonal energy consumption for the entire system relative to single-stage and three-stage systems, respectively. 3. When the system selectively uses exhaust air or outdoor air, 11.0% of additional seasonal energy consumption can be saved for the entire system relative to a system using only outdoor air.

Experimental studies on the flow pattern regimes of particles-liquid mixtures in an inner circulation vertical mill

A vertical mill platform for inner circulation is constructed to improve the grinding efficiency. A transparent plexiglass grinding chamber is utilized to study the influence of solids density, particle size, solid volume fraction and agitator speed on the variation and transition of flow pattern regimes. The change regulations of rotor speed to just circulation and complete circulation as well as system energy consumption are analyzed, the empirical correlations for predicting the rotor speed to just circulation and complete circulation are proposed based on the experimental data of particles-liquid mixtures. The results indicate that the flow of particles-liquid mixture shows four different flow patterns with the increase of the agitator speed. In addition, the process of transition between flow patterns is different when the volume fraction of particles is lower or higher than 20%. The just circulation speed and the complete circulation speed of particles increase as the volume fraction of particles increases. The total system power consumption increases slowly when the volume fraction of particles is lower than 40%; but it increases sharply if the solid volume fraction exceeds 40%.

Enhancing Network Security through Software Defined Networking (SDN)

The IoT connects many of the home appliances in the world, from intelligent thermostats to intelligent vehicles. By the end of 2015, we had 9 billion connected stuff. The Gartner forecast shows that the number of devices in the network will exceed 50 billion by 2020. Most of the connected devices in the existing network are based on obsolete security infrastructure and encryption, while many do not have provisions for remote updating of these devices. Taking into account the number of IoT devices, from off-shelf motion monitoring to machine tools, it is not possible to check which functions end up with any specific service or product. In short, this is the problem: you can't trust the integrity of the security of the device and determine exactly where the data is processed and stored. The SDN architecture is designed to increase the routing and networking performance of the existing networks by isolating the control plane from the data plane. The basic concept of Software Defined Networking can be applied to IoT Multi networks; however, the standard SDN implementations for wired networks are not directly suitable for distributed and ad-hoc mesh networks, which are common in IoT systems. However, the SDN architecture changes the pattern of communication of the IoT network, leading to a new approach to the manifestation of the Securities IoT network. Centralized data layer control and control layer not only simplifies data package management, but also increases safety. As mentioned above, the amount of data that flows into these systems is significant. Proper management of traffic and load balancing will help to simplify the data flow in some ways. The total system's power consumption may be reduced by the central station, which requires less power than the distributed control. To sum up, introducing SDN into IoT will help IoT and stimulate IoT in people's regular lives.

A 0.6V 785-nW Multimodal Sensor Interface IC for Ozone Pollutant Sensing and Correlated Cardiovascular Disease Monitoring

In this article, we present the design and analysis of a 785-nW multimodal sensor interface IC for ozone pollutant sensing and correlated cardiovascular disease monitoring based on electrocardiography (ECG) and photoplethysmography (PPG). The proposed hybrid dc offset current cancellation (DCOC) along with a 4- MΩ gain-regulated cascode transimpedance amplifier (RGC-TIA) enable PPG readout power reduction by 37×, compared with the state-of-the-art PPG sensor interfaces. The ozone sensing channel proposes an adaptive architecture to enable low V <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DD</sub> operation, achieving a 300× power reduction, compared with the state-of-the-art gas sensing readouts. The ozone sensing channel's performance was also verified using custom resistive metal-oxide sensors for concentrations from 50 to 900 ppb. The sensor interface IC is fabricated in a 65-nm CMOS, integrating a 165-nW voltage-mode ECG channel, a 532-nW current-mode PPG channel, 76-nW resistive-mode ozone channel, and 12.6-nW peripheral circuits, all at 0.6 V. The total system power consumption including the LED and a custom digital readout IC is 10.98- 15.51 μW, which is 41×- 57× less than prior ozone/CVD joint monitoring sensor interface systems.

A Fully Integrated Reprogrammable CMOS-RRAM Compute-in-Memory Coprocessor for Neuromorphic Applications

Analog compute-in-memory with resistive random access memory (RRAM) devices promises to overcome the data movement bottleneck in data-intensive artificial intelligence (AI) and machine learning. RRAM crossbar arrays improve the efficiency of vector-matrix multiplications (VMMs), which is a vital operation in these applications. The prototype IC is the first complete, fully integrated analog-RRAM CMOS coprocessor. This article focuses on the digital and analog circuitry that supports efficient and flexible RRAM-based computation. A passive $54\times108$ RRAM crossbar array performs VMM in the analog domain. Specialized mixed-signal circuits stimulate and read the outputs of the RRAM crossbar. The single-chip CMOS prototype includes a reduced instruction set computer (RISC) processor interfaced to a memory-mapped mixed-signal core. In the mixed-signal core, ADCs and DACs interface with the passive RRAM crossbar. The RISC processor controls the mixed-signal circuits and the algorithm data path. The system is fully programmable and supports forward and backward propagation. As proof of concept, a fully integrated 0.18- $\mu \text{m}$ CMOS prototype with a postprocessed RRAM array demonstrates several key functions of machine learning, including online learning. The mixed-signal core consumes 64 mW at an operating frequency of 148 MHz. The total system power consumption considering the mixed-signal circuitry, the digital processor, and the passive RRAM array is 307 mW. The maximum theoretical throughput is 2.6 GOPS at an efficiency of 8.5 GOPS/W.

Power Consumption Minimization in Cache-Enabled Mobile Networks

Caching at base stations (BSs) can enhance the performance of a mobile network, which has gained much attention in the past few years. In this paper, we investigate the energy-saving issue in the cache-enabled mobile network, where we try to minimize the total system power consumption with limited radio resource and storage capacity of the BSs. We decouple the formulated difficult optimization task into a series of tractable subproblems and develop efficient algorithms to solve them with reasonable computation load. The key idea of our proposed scheme is to associate as many as possible users with the BSs that have stored the users’ requested files in their caches while considering the spectrum and power budgets of the BSs. Numerical results show our proposed scheme can significantly reduce the system power consumption compared with others.

Nature‐inspired optimisation algorithms assisted realisation of green communication via CR:a comparison study

Cognitive radio (CR) technology enables adaptation of transmission parameters according to the operating environment and different quality of service (QoS) requirements. This feature can be applied to realise green radios as recent research trends are focused on improving the energy efficiency of wireless communication systems. A power amplifier (PA) is found to consume a major portion of energy in radio-frequency circuits for medium- and long-range transmissions. Thus, minimising PA power consumption is one of the major challenges to realise green radios. In this study, mathematical formulation of total system power consumption at CR transmitter with Class B PA is shown and its optimisation is done by parameter reconfiguration for data transmission scenario employing recently proposed nature-inspired optimisation techniques. Performance of ant lion optimiser, grasshopper optimisation algorithm, Grey wolf optimiser, Moth-flame optimisation and Whale optimisation algorithm (WOA) is compared for solving this problem in terms of different performance metrics. Simulation results show the effectiveness of WOA in minimising the system power consumption by parameter adaptation in multicarrier CR system while satisfying different QoS constraints.

An energy-efficient low-memory image compression system for multimedia IoT products

Emerging Internet of things (IoT) technologies have rapidly expanded to multimedia applications, including high-resolution image transmission. However, handling image data in IoT products with limited battery capacity requires low-complexity and small-size solutions such as low-memory compression techniques. The objective of this paper is to propose a line-based compression system based on four-level two-line discrete wavelet transform and adaptive line prediction. Bit stream is generated by multiplexing various frequency components with run-level coding combined with Huffman coding. The proposed system also includes a new bit rate control algorithm that could significantly improve image quality consistency in one frame. The proposed low-memory compression system can retain image quality for visually lossless compression criteria over the whole image frame. It can simultaneously lower total system power consumption in multimedia IoT products better than other existing low-memory compression techniques.

Power-Efficient and Secure WPCNs With Hardware Impairments and Non-Linear EH Circuit

In this paper, we design a robust resource allocation algorithm for a wireless-powered communication network (WPCN) taking into account residual hardware impairments (HWIs) at the transceivers, the imperfectness of the channel state information, and the non-linearity of practical radio frequency energy harvesting circuits. In order to ensure power-efficient secure communication, physical layer security techniques are exploited to deliberately degrade the channel quality of a multiple-antenna eavesdropper. The resource allocation algorithm design is formulated as a non-convex optimization problem for minimization of the total power consumption in the network, while guaranteeing the quality of service of the information receivers in terms of secrecy rate. The globally optimal solution of the optimization problem is obtained via a 2-D search and semidefinite programming relaxation. To strike a balance between computational complexity and system performance, a low-complexity iterative suboptimal resource allocation algorithm is also proposed. Numerical results demonstrate that both the proposed optimal and suboptimal schemes can significantly reduce the total system power consumption required for guaranteeing secure communication, and unveil the impact of HWIs on the system performance: 1) residual HWIs create a system performance bottleneck in WPCN in the high transmit/receive power regimes; 2) increasing the number of transmit antennas can effectively reduce the power consumption of wireless power transfer and alleviate the performance degradation due to residual HWIs; and 3) imperfect CSI exacerbates the impact of residual HWIs, which increases the power consumption of both wireless power and wireless information transfer.

Resource Allocation in Multi-User MIMO-OFDM Systems with Double-objective Optimization

A resource allocation algorithm is proposed in this paper to simultaneously minimize the total system power consumption and maximize the system throughput for the downlink of multi-user multiple input multiple output-orthogonal frequency division multiplexing (MIMO-OFDM) systems. With the Lagrange dual decomposition method, we transform the original problem to its convex dual problem and prove that the duality gap between the two problems is zero, which means the optimal solution of the original problem can be obtained by solving its dual problem. Then, we use convex optimization method to solve the dual problem and utilize bisection method to obtain the optimal dual variable. The numerical results show that the proposed algorithm is superior to traditional single-objective optimization method in both the system throughput and the system energy consumption.

Downlink Transmission Strategies in Power-Splitting SWIPT Distributed MISO Systems

This paper investigates the joint beamforming and power splitting factor optimization in distributed multiple input single output systems with power-splitting simultaneous wireless information and power transfer (SWIPT). We assume that all the user equipment (UEs) perform data transmission, whereas only UEs with sufficient large scale fading, defined as SWIPT UEs, also conduct energy harvesting. System total power consumption minimization problem is formulated with per-UE quality of service (QoS) constraints and minimal harvesting power constraints for SWIPT UEs with the consideration of RAU selection. First, we classify the UEs into two groups according to the large scale fading criterion. Then for perfect channel state information (CSI), the optimal problem is formulated and approximated by transforming RAU mode indicator function into reweighted $\ell _{0}$ -norm. Semi-definite relaxed method is used and an iterative algorithm is proposed. Furthermore, we consider the optimization with imperfect CSI and change the constraints to the worst case. To guarantee the rank-one constraint, an iterative function based on penalty function is proposed. Simulations show that the proposed algorithms with RAU selection can significantly reduce system total power consumption and also reveal that the RAU closest to SWIPT UEs tends to be active.

System-Level Power Consumption Analysis of the Wearable Asthmatic Wheeze Quantification

Long-term quantification of asthmatic wheezing envisions an m-Health sensor system consisting of a smartphone and a body-worn wireless acoustic sensor. As both devices are power constrained, the main criterion guiding the system design comes down to minimization of power consumption, while retaining sufficient respiratory sound classification accuracy (i.e., wheeze detection). Crucial for assessment of the system-level power consumption is the understanding of trade-off between power cost of computationally intensive local processing and communication. Therefore, we analyze power requirements of signal acquisition, processing, and communication in three typical operating scenarios: (1) streaming of uncompressed respiratory signal to a smartphone for classification, (2) signal streaming utilizing compressive sensing (CS) for reduction of data rate, and (3) respiratory sound classification onboard the wearable sensor. Study shows that the third scenario featuring the lowest communication cost enables the lowest total sensor system power consumption ranging from 328 to 428 μW. In such scenario, 32-bit ARM Cortex M3/M4 cores typically embedded within Bluetooth 4 SoC modules feature the optimal trade-off between onboard classification performance and consumption. On the other hand, study confirms that CS enables the most power-efficient design of the wearable sensor (216 to 357 μW) in the compressed signal streaming, the second scenario. In such case, a single low-power ARM Cortex-A53 core is sufficient for simultaneous real-time CS reconstruction and classification on the smartphone, while keeping the total system power within budget for uncompressed streaming.

Theoretical study of a high-temperature heat pump system composed of a CO2 transcritical heat pump cycle and a R152a subcritical heat pump cycle

A high-temperature heat pump system composed of a CO2 transcritical cycle and a R152a subcritical cycle is proposed for heat and cooling cogeneration. Emphasis is placed on analyzing and comparing thermodynamic performance of the combined system to the single heat pump and singe CO2 refrigeration systems working alone. Entransy dissipation rate and T-Q diagram are also applied to illustrate temperature match behavior of heating process of supply water. Besides, performance comparisons were also conducted between the systems with and without intermediate heat exchanger and between the systems with different working fluids. The studies indicate that there exist appropriate ranges of CO2 discharging pressure for CO2 transcritical cycle to meet the requirements of pinch point temperatures for heat transfer processes. Increasing CO2 discharging pressure, both the system heating and cooling capacities are increased. However, total system power consumption first slightly decreases and then increases exponentially. As for the overall system COP and exergy efficiency, both are increased with increasing CO2 discharging pressure. Under the same operating conditions, the system COP and exergy efficiency of this combined system are increased by about 54.7% and 175%, respectively, as compared to the single R152a heat pump and single CO2 transcritical cycle working alone.

Power Efficient Resource Allocation for Full-Duplex Radio Distributed Antenna Networks

In this paper, we study the resource allocation algorithm design for distributed antenna multiuser networks with full-duplex (FD) radio base stations (BSs), which enable simultaneous uplink and downlink communications. The considered resource allocation algorithm design is formulated as an optimization problem taking into account the antenna circuit power consumption of the BSs and the quality of service (QoS) requirements of both uplink and downlink users. We minimize the total network power consumption by jointly optimizing the downlink beamformer, the uplink transmit power, and the antenna selection. To overcome the intractability of the resulting problem, we reformulate it as an optimization problem with decoupled binary selection variables and nonconvex constraints. The reformulated problem facilitates the design of an iterative resource allocation algorithm, which obtains an optimal solution based on the generalized Bender’s decomposition (GBD). For this algorithm, we also propose a simple technique to improve the speed of convergence. Furthermore, to strike a balance between computational complexity and system performance, a suboptimal resource allocation algorithm with polynomial time complexity is proposed. Simulation results illustrate that the proposed GBD-based iterative algorithm converges to the globally optimal solution and the suboptimal algorithm achieves a close-to-optimal performance. Our results also demonstrate the tradeoff between power efficiency and the number of active transmit antennas when the circuit power consumption is taken into account. In particular, activating an exceedingly large number of antennas may not be an efficient approach for reducing the total system power consumption. In addition, our results reveal that FD systems facilitate significant power savings compared to traditional half-duplex systems, despite the nonnegligible self-interference.

Energy Efficient Forward and Backhaul Link Optimization in OFDMA Small Cell Networks

This letter aims at improving the energy efficiency of OFDMA small cell networks with data rate constraints. Different with the existing energy efficiency works that focus on the base station side, the effect of backhaul links is analyzed based on a bufferless data flow model. A joint forward and backhaul link optimization (JFBLO) scheme is proposed to iteratively increase the system energy efficiency by optimizing the emission power and backhaul data rate. The system energy efficiency is proved to be non-decreasing and convergent in JFBLO. Simulation results indicate that though occupying a small share of the total system power consumption, the emission power is necessary to be optimized to improve the system energy efficiency when the effect of backhaul links is considered.

Energy Efficient Cooperative Sleep Control Using Small Cell for Wireless Networks

The demand for higher throughput and more reliable quality of service (QoS) explosive increases the number of base stations in future wireless networks, which causes severe energy waste problems in wireless networks, especially when base stations are in low traffic load status. This paper has two main contributions: first of all, we propose a semistatic energy efficient method to find out the optimal on-off pattern considering the interference from adjacent base stations using integer convex optimization, from which the optimal on-off proportion of base stations could be obtained; then we propose a novel energy efficient cooperative heuristic algorithm in dynamic scenario, by separating the waiting time into two independent Markov processes using the new method; the total power consumption could be reduced significantly by adopting the novel method. Compared with reference method, simulation results indicate that in semistatic scenario, the total system power consumption could be reduced up to 26%, and, in dynamic scenario, the system power consumption could be reduced up to 40%.

Power Consumption Optimization Technique in ACS for Space Time Trellis Code Viterbi Decoder

To provide fast digital communications systems, energy efficient, high-performance, low power is critical for decoding mobile receiver device. This paper proposes a low power optimization techniques in the Add Compare Select (ACS) unit for Space Time trellis codes (STTC) Viterbi decoder. STTC Viterbi decoder is used as a reference case. This paper discusses about how to lower the power in the ACS architecture, to optimize the Viterbi decoder STTC in reducing the total power consumption. Based on the results of design and analysis, power consumption Viterbi decoder modeling, low power system for STTC Viterbi decoder is proposed. Design and optimization of ACS unit in STTC Viterbi decoding is done using Verilog HDL language. Power analysis tools in the software Altera Quartus 2 is used for the synthesis of total system power consumption. Optimization strategy showed an increase of 83% power reduction compared to previous studies.