Concept Evolution and Multi-Dimensional Measurement Comparison of Urban Energy Performance from the Perspective of System Correlation: Empirical Analysis of 142 Prefecture Level Cities in China

In order to clarify the evolution characteristics and direction of urban energy performance concepts, reveal the research dimensions, determine the performance results and differences, and clarify the reference benchmark, this study depicts the main systems involved in the process of urban energy utilization, demonstrates their relevance guided by the system view, and proposes the measurement indicators in the economic, environmental, and well-being dimensions. The measurement model of each dimension is constructed using the corresponding models of Data Envelopment Analysis. Taking 142 prefecture level cities in China as examples, the energy performance in different dimensions is measured and compared. The energy performance levels are close in the economic and environmental dimensions. However, the results of the well-being dimension are different from these first two dimensions, and the performance levels among cities differ more. In the economic, environmental, and well-being dimension, 22, 28 and 16 cities have reached the effective frontier, respectively, and the performance benchmark cities of 15, 15 and 5 provinces are non-provincial capital cities, respectively. Based on the above analysis, the “chain” framework evolution direction of concept and measurement is proposed, and this study provides benchmarks and policy suggestions to improve energy performance.

Queue Management Benchmark for Modelling Patients' Flow in Nigerian Public Hospitals

Although queue management in hospitals is widely researched, little is known about the benchmark for modelling patients flow in terms of the optimal number of servers required for effective service delivery. This study applied the queuing theory to the Nigerian public hospitals by setting a benchmark for modelling patients flow. A mixture of survey and observation was adopted to garner data for 30 days from patients in six public hospitals in Nigeria. Data were subjected to performance analysis via the Temporary Ordered Routine Algorithm. The computed performance values were further compared with their acceptable benchmarks for multi-server queues through the General Purpose System Simulator. We found the queuing system in the select hospitals not in congruence with the system performance benchmark; the mean service rate in each facility was low compared to the mean arrival rate; and the simulated number of doctors for were below the modelled benchmark. Managerial implications of findings were discussed.

A Runtime Reconfigurable Design of Compute-in-Memory–Based Hardware Accelerator for Deep Learning Inference

Compute-in-memory (CIM) is an attractive solution to address the “memory wall” challenges for the extensive computation in deep learning hardware accelerators. For custom ASIC design, a specific chip instance is restricted to a specific network during runtime. However, the development cycle of the hardware is normally far behind the emergence of new algorithms. Although some of the reported CIM-based architectures can adapt to different deep neural network (DNN) models, few details about the dataflow or control were disclosed to enable such an assumption. Instruction set architecture (ISA) could support high flexibility, but its complexity would be an obstacle to efficiency. In this article, a runtime reconfigurable design methodology of CIM-based accelerators is proposed to support a class of convolutional neural networks running on one prefabricated chip instance with ASIC-like efficiency. First, several design aspects are investigated: (1) the reconfigurable weight mapping method; (2) the input side of data transmission, mainly about the weight reloading; and (3) the output side of data processing, mainly about the reconfigurable accumulation. Then, a system-level performance benchmark is performed for the inference of different DNN models, such as VGG-8 on a CIFAR-10 dataset and AlexNet GoogLeNet, ResNet-18, and DenseNet-121 on an ImageNet dataset to measure the trade-offs between runtime reconfigurability, chip area, memory utilization, throughput, and energy efficiency.

Generalized Correntropy Criterion-Based Performance Assessment for Non-Gaussian Stochastic Systems

Control loop performance assessment (CPA) is essential in the operation of industrial systems. In this paper, the shortcomings of existing performance assessment methods and indicators are summarized firstly, and a novel evaluation method based on generalized correntropy criterion (GCC) is proposed to evaluate the performance of non-Gaussian stochastic systems. This criterion could characterize the statistical properties of non-Gaussian random variables more fully, so it can be directly used as the assessment index. When the expected output of the given system is unknown, generalized correntropy is used to describe the similarity of two random variables in the joint space neighborhood controlled and take it as the criterion function of the identification algorithms. To estimate the performance benchmark more quickly and accurately, a hybrid-EDA (H-EDA) combined with the idea of “wading across the stream algorithm” is proposed to obtain the system parameters and disturbance noise PDF. Through the simulation of a single loop feedback control system under different noise disturbances, the effectiveness of the improved algorithm and new indexes are verified.

NeuroSim Simulator for Compute-in-Memory Hardware Accelerator: Validation and Benchmark

Compute-in-memory (CIM) is an attractive solution to process the extensive workloads of multiply-and-accumulate (MAC) operations in deep neural network (DNN) hardware accelerators. A simulator with options of various mainstream and emerging memory technologies, architectures, and networks can be a great convenience for fast early-stage design space exploration of CIM hardware accelerators. DNN+NeuroSim is an integrated benchmark framework supporting flexible and hierarchical CIM array design options from a device level, to a circuit level and up to an algorithm level. In this study, we validate and calibrate the prediction of NeuroSim against a 40-nm RRAM-based CIM macro post-layout simulations. First, the parameters of a memory device and CMOS transistor are extracted from the foundry’s process design kit (PDK) and employed in the NeuroSim settings; the peripheral modules and operating dataflow are also configured to be the same as the actual chip implementation. Next, the area, critical path, and energy consumption values from the SPICE simulations at the module level are compared with those from NeuroSim. Some adjustment factors are introduced to account for transistor sizing and wiring area in the layout, gate switching activity, post-layout performance drop, etc. We show that the prediction from NeuroSim is precise with chip-level error under 1% after the calibration. Finally, the system-level performance benchmark is conducted with various device technologies and compared with the results before the validation. The general conclusions stay the same after the validation, but the performance degrades slightly due to the post-layout calibration.

Unlocking the Residential Retrofitting Potential in a Three-Degree World: A Holistic Approach to Passive Design in Hot Climates

The Kingdom of Saudi Arabia (KSA), as one of the largest polluters worldwide, has released its Vision 2030 that seeks sustainable development via economic diversification to transition towards lower CO2 energy systems. Due to fast population and economic growth, the Kingdom is undergoing an increasing volume of construction, which is projected to exacerbate the energy-related emissions. Strategies are needed to decarbonise the housing stock and help bridge the existing performance gap with the updated Saudi Building Code (SBC). This study proposes a holistic retrofitting approach for the Saudi building industry to facilitate the identification of energy consumption reduction optimisation solutions, covering the assessment of insulation, reflective coating surfaces, sun shading devices, efficient glazing solutions, building-integrated renewables, and green roofs. The proposed flexible approach proved how blended retrofit packages provide improved performance, with rooftop photovoltaic microgeneration and improved glazing technologies singlehandedly outperforming the remaining proposals for KSA’s Riyadh climate conditions. Only the photovoltaic system could meet the simulated SBC performance benchmark independently, positioning it as an instrumental tool in improving the overall effectiveness of the retrofit packages.