Microarchitecture and Design Challenges for Gigascale Integration

Technology scaling continues providing integration capacity of billions of transistor; however, power delivery and dissipation are the barriers. Supply voltage scaling - which provides relief in active power reduction - has to slow down to limit subthreshold leakage. A variation in process, temperature, and supply voltage forces a major change in the design paradigm, from deterministic to probabilistic design. Therefore, business as usual is not an option. In this paper we address leakage problem and provide various leakage reduction techniques, which include a dynamic sleep transistor and a dynamic body bias technique. In addition, we discuss advances in circuits and microarchitectures to exploit future gigascale integration capacity for a system on a chip (SOC) by an experimental 10Gbps Ethernet TCP/IP processor, to help integrate diverse functional blocks, providing valued performance, with better power efficiency and design productivity.

The article is devoted to consideration of issues related to assessing the energy efficiency of a coal mining enterprise. Coal mining enterprises are characterized by a large volume of energy intensive ore mining operations. The article presents possible ways of energy saving in the mining industry. During the research, a system of energy efficiency indicators for coal mining enterprises is presented. Materials and methods. The article proposes a system of indicators for coal enterprises based on existing methods and approaches to assessing energy efficiency. Energy consumption dependence graphs, benchmarking, and pinch analysis are considered as a starting point. Results. The study presents a system of energy efficiency indicators for coal mining enterprises. The proposed system has several levels. The existing methods and approaches for assessing this important indicator for industrial enterprises in the coal industry as a whole are considered. The author’s approach to formalizing the system of energy efficiency indicators for coal mining enterprises is proposed. The significance of energy audit as a key assessment measure is indicated and its stages are highlighted. The influencing factors determining energy costs and their reduction for a coal mining enterprise are systematized, barriers preventing the practical implementation of energy saving measures are analyzed. Discussion. The proposed metrics system provides a comprehensive framework for assessing the energy efficiency of coal mining enterprises at various levels. Its flexibility allows it to be applied to the analysis of individual equipment units, production processes, and overall enterprise performance. Its multi-level approach provides a detailed understanding of energy consumption, which allows for informed decision-making to optimize operations. At the equipment level, the metrics system evaluates the energy consumption and efficiency of individual machines and devices. At the production process level, the metrics system analyzes the energy consumption and efficiency of various stages of coal mining. At the enterprise level, the metrics system provides a comprehensive assessment of overall energy efficiency. It takes into account energy consumption at all levels and allows enterprises to compare their performance with each other and with industry benchmarks. Conclusion. Energy efficiency assessment of a coal mining enterprise allows to understand how and where energy is used, where its use can be improved, and how to reduce its costs. To conduct an energy efficiency assessment, the article proposes a system of indicators, highlighting the features of an energy audit. The main advantage of the proposed system of indicators is that it covers a wide range of energy consumption indicators, which are carefully selected and located at different levels of detail. In general, a balanced approach to the level of detail and generalization in the system of indicators provides flexibility in adapting indicators to specific research questions and helps to interpret the results in the context of structural differences between energy systems. Resume. Overall, the proposed metrics system offers a comprehensive approach to analyzing the energy efficiency of coal mining enterprises, covering all levels of operations. Its use allows enterprises to improve energy efficiency, reduce operating costs, and contribute to a more sustainable coal mining industry. Suggestions for practical applications and directions for future research. The use and implementation of the developed system at a coal mining company allows it to improve energy efficiency, reduce operating costs and contribute to a more sustainable coal mining industry.

Research on the building energy efficiency design strategy of Chinese universities based on green performance analysis

Consumer electronics products, such as high-end (digital) TVs, contain complex systems on chip (SOC) that offer high computational performance at low cost. Traditionally, these SOCs are application-specific standard products (ASSPs) with limited programmability We describe why TV SOCs must become more flexible, and why companion chips together with networks on chips (NOC) are a crucial enabling technology In particular, networks that span multiple chips will become important in the near future. We demonstrate our ideas by extending a commercially-available SOC for picture improvement in high-end TVs with the /Ethereal NOC. Our first unoptimised results indicate that replacing the original interconnect (consisting of dedicated links and multiplexers for bypasses) by programmable NOC increases the SOC area by 4% and its power dissipation by 12%. The new, flexible SOC allows new tasks to be spliced in at any point in the task graph. Both analytical performance verification and system simulations at RTL VHDL show that the extended SOC meets its functional requirements. Using the /Ethereal design flow the extended architecture was designed, implemented, and verified in 12 person months. To the best of our knowledge, this is the first application of a NOC to a commercial SOC. The quantitative results indicate that even retrofitting a NOC to an existing architecture is beneficial at acceptable cost

This article according to the composition concept of System on Chip (SoC), carries on the patent analysis in U.S and Taiwan. The article had recognized SoC international patent classification (IPC) in national, owner's distribution by the objective statistical analysis. It had understood the SoC technological development's process, also clearly observe research and develop organization's possible structure of the patent owners. Findings discovery are: 1. The SoC technology's development is at present in the growth period of the front. 2. The U.S has the constitutive design and reliable confirmation technical lead in the SoC technology's development, i.e. system on chip application plan ability. 3. Taiwan has the outstanding lead in SoC chip's manufacture and spare part conformity processing technology. 4. In SoC technology research and development output, the major part was the research and development team joint effort achievement, only had the few parts was concentrated in the specific inventor.

Highlights on energy efficiency improvements: a case of a small brewery

This paper proposes a FAst paRAllel and pipeliNE Q-learning accelerator (FARANE-Q) for a configurable Reinforcement Learning (RL) algorithm implemented in a System on Chip (SoC). The proposed work offers flexibility, configurability, and scalability while maintaining computation speed and accuracy to overcome the challenges of a dynamic environment and increasing complexity. The proposed method includes a Hardware/Software (HW/SW) design methodology for the SoC architecture to achieve flexibility. We also propose joint optimizations on the algorithm, architecture, and implementation to obtain optimum (high efficiency) performance, specifically in energy and area efficiency. Furthermore, we implemented the proposed design in a real-time Zynq Ultra96-V2 FPGA platform to evaluate the functionality with an actual use case of smart navigation. Experimental results confirm that the proposed accelerator FARANE-Q outperforms state-of-the-art works by achieving a throughput of up to 148.55 MSps. It corresponds to the energy efficiency of 1747.64 MSps/W per agent for 32-bit and 2424.33 MSps/W per agent for 16-bit FARANE-Q. Moreover, the proposed 16-bit FARANE-Q outperforms other related works by an improvement of at least <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.23\times $ </tex-math></inline-formula> in energy efficiency. The designed system also maintains an error accuracy of less than 0.4% with optimized bit precision for more than eight fraction bits. The proposed FARANE-Q also offers a speed up of processing time up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1795\times $ </tex-math></inline-formula> compared to embedded SW computation executed on ARM Zynq processor and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$280\times $ </tex-math></inline-formula> of computation of full software executed on i7 processor. Hence, the proposed work has the potential to be used for smart navigation, robotic control, and predictive maintenance.

Gigascale Integration (GSI) enables a new generation of monolithic focal plane processing systems built with billion-transistor chips. As this technology matures, fundamental technology limitations on wire interconnects and power dissipation will become the performance bottleneck. This paper presents system performance projections for GSI technologies under these constraints. Architectural models and workload characterization are integrated to identify viable future system implementations. The SIMD Pixel processor (SIMPil) is selected as the architecture for evaluation, and an image processing application suite is programmed to characterize the workload. Projections for SIMPil systems show that over three orders of magnitude improvement is achievable by 2012 in both system throughput and image resolution. System power consumption is contained below 50 W for a 52,900 processor system in 50 nm technology. The SIMPil architecture design space is explored, and opportunities for more aggressive designs within power density limits are examined.

The realization of sustainable development depends on the efficient use of energy resources. Energy consumption productivity and efficiency are among important indices that play a critical role in explaining the behavior of energy consumption structure of different economic sectors, which in turn, plays a key role in policy making. From among economic sectors, the agriculture sector, as an important sector, depends on the consumption of energy as a factor underpinning agricultural production to a great extent. Thus, the present study tries to firstly calculate energy efficiency and productivity of agricultural sector, and then, investigate the relationship between production and energy consumption of New Zealand’s agricultural sector using simultaneous equations system and two-stage least squares method. The annual data of New Zealand agricultural economics are used for the period of 1990-2017. The results showed that agricultural sector’s energy consumption efficiency in New Zealand was mostly optimum according to energy efficiency index. In addition, estimation of energy equation revealed that the added value of agricultural sector had a positive influence on energy consumption growth. Among the other positively affecting variables, agricultural sector’s labour and energy consumption in the prior period could be mentioned. The negative sign of capital stock in the equation reflects the fact that the higher the investment in agricultural sector, the lower the energy consumption.

Efficient management of heating, ventilation, and air conditioning (HVAC) systems is paramount for mitigating energy consumption and enhancing climate resilience in modern infrastructure. This review explores the significance of leveraging project management tools to optimize energy efficiency within HVAC operations, thus fostering a pathway towards climate resilience. The integration of project management methodologies offers a systematic approach to address challenges associated with energy consumption in HVAC systems, thereby facilitating informed decision-making processes. Firstly, the review delves into the pressing need for energy conservation in HVAC operations amidst escalating concerns over climate change and its adverse impacts. The imperative to minimize energy consumption in buildings, particularly through HVAC systems, is highlighted as a pivotal step towards achieving climate resilience and sustainability goals. Subsequently, the review outlines the role of project management tools in orchestrating effective strategies to enhance energy efficiency within HVAC operations. By employing techniques such as project scheduling, risk management, and resource allocation, project managers can streamline the implementation of energy-efficient measures, thereby optimizing HVAC system performance and reducing carbon emissions. Moreover, the review underscores the importance of data analytics and technological advancements in augmenting the efficacy of project management tools for energy efficiency in HVAC operations. Leveraging real-time data monitoring, predictive analytics, and IoT-enabled devices enables proactive maintenance and continuous optimization of HVAC systems, thereby maximizing energy savings and bolstering climate resilience. Furthermore, the review elucidates the potential benefits and challenges associated with the adoption of project management tools for energy efficiency in HVAC operations. While improved cost-effectiveness, environmental sustainability, and operational performance emerge as primary benefits, challenges such as initial investment costs, technological complexities, and organizational inertia necessitate careful consideration. This review advocates for the integration of project management tools as a viable approach to foster energy efficiency in HVAC operations, thereby paving the way towards enhanced climate resilience and sustainability in built environments. By embracing innovative methodologies and technological solutions, stakeholders can mitigate energy consumption, reduce greenhouse gas emissions, and fortify infrastructure against the impacts of climate change. Keywords: HVAC, Climate Resilience, Project Management, Energy, Review.

The effect of applying an energy pulse to the heart during ventricular fibrillation is described by the probability of successful defibrillation or success rate. Seven to ten (8.60 +/- 0.84: mean +/- standard deviation) defibrillation trials per energy were randomly attempted at energies which span the defibrillation success rate versus energy curve. We obtained 70.0 +/- 8.4 episodes per dog. We fit the defibrillation success rate versus energy relationship from ten dogs (20.5 +/- 1.5 kg) to four types of curves: linear, exponential, probit transformed linear, and logit transformed linear. The correlation coefficients for each fit are 0.917 +/- 0.057, 0.944 +/- 0.014, 0.926 +/- 0.51, and 0.889 +/- 0.098, respectively. We therefore conclude that the exponential curve best describes the DSRE relationship. This suggests the existence of an energy below which defibrillation does not occur. At higher energies, the exponential curve asymptotically approaches a 100% success rate, which indicates that increasing the energy produces a diminishing benefit to defibrillation success rate. The estimated energies with a 0% defibrillation success rate are surprisingly consistent among dogs, with 2.072 +/- 0.553 J. The estimated energy with an 80% defibrillation success rate is 5.217 +/- 1.091 J. The estimated defibrillation success rate corresponding to the defibrillation threshold of 3.59 +/- 1.06 J is consistent with 0.516 +/- 0.144. The estimated energies with a 0% success rate correlate well with the defibrillation thresholds with R = 0.772; P = 0.0088. Since implantable defibrillators have a limited energy supply, we determined energy efficiency by dividing defibrillation success rate by the applied energy and energy consumption by dividing the applied energy by the defibrillation success rate. The most efficient defibrillation energy occurs at the maximum energy efficiency and the minimum energy consumption. The most efficient defibrillation energy of 4.34 +/- 0.97 J determined from the exponential fit has a success rate of 0.70 +/- 0.06. The most efficient defibrillation energy can be predicted from the defibrillation threshold. Clinically, a 70% success rate may not be adequate. We, therefore, compared the energy efficiency and consumption of energies with 90% and 95% success rates to the most efficient defibrillation energy. About a 50% increase in energy from the most efficient defibrillation energy is necessary for a 90% success rate which results in about a 13% loss in energy efficiency and about a 16% increase in energy consumption. About an 84% energy increase is necessary for a 95% success rate which results in about a 24% loss in energy efficiency and about a 33% increase in energy consumption.(ABSTRACT TRUNCATED AT 400 WORDS)

Purpose – This paper aims to describe the creation of innovative and intelligent systems to optimise energy efficiency in manufacturing. The systems monitor energy consumption using ambient intelligence (AmI) and knowledge management (KM) technologies. Together they create a decision support system as an innovative add-on to currently used energy management systems. Design/methodology/approach – Energy consumption data (ECD) are processed within a service-oriented architecture-based platform. The platform provides condition-based energy consumption warning, online diagnostics of energy-related problems, support to manufacturing process lines installation and ramp-up phase and continuous improvement/optimisation of energy efficiency. The systems monitor energy consumption using AmI and KM technologies. Together they create a decision support system as an innovative add-on to currently used energy management systems. Findings – The systems produce an improvement in energy efficiency in manufacturing small- and medium-sized enterprises (SMEs). The systems provide more comprehensive information about energy use and some knowledge-based support. Research limitations/implications – Prototype systems were trialled in a manufacturing company that produces mooring chains for the offshore oil and gas industry, an energy intensive manufacturing operation. The paper describes a case study involving energy-intensive processes that addressed different manufacturing concepts and involved the manufacture of mooring chains for offshore platforms. The system was developed to support online detection of energy efficiency problems. Practical implications – Energy efficiency can be optimised in assembly and manufacturing processes. The systems produce an improvement in energy efficiency in manufacturing SMEs. The systems provide more comprehensive information about energy use and some knowledge-based support. Social implications – This research addresses two of the most critical problems in energy management in industrial production technologies: how to efficiently and promptly acquire and provide information online for optimising energy consumption and how to effectively use such knowledge to support decision making. Originality/value – This research was inspired by the need for industry to have effective tools for energy efficiency, and that opportunities for industry to take up energy efficiency measures are mostly not carried out. The research combined AmI and KM technologies and involved new uses of sensors, including wireless intelligent sensor networks, to measure environment parameters and conditions as well as to process performance and behaviour aspects, such as material flow using smart tags in highly flexible manufacturing or temperature distribution over machines. The information obtained could be correlated with standard ECD to monitor energy efficiency and identify problems. The new approach can provide effective ways to collect more information to give a new insight into energy consumption within a manufacturing system.

This paper deals with low power multiplier design that plays a significant role in green wireless communications systems. Over the period of time, researchers have proposed various multiplier designs in order to get high speed. Vedic multiplier is considered as one of the low power multiplier along with high speed as compared with traditional array and booth multipliers. Vedic Multiplier contains a total of sixteen algorithms/sutras for predominantly logical operations. This research focuses on thermal aspects and energy efficiency of wireless communications systems with the thermal aware low power design of Vedic Multiplier. Temperature plays an important role on the performance of any device. The primary purpose of this research is to enhance the thermal stability of the wireless communications. Energy efficient IO standards are used to decrease the power dissipation of Vedic Multiplier and that eventually decrease power dissipation of wireless communications systems. In order to study the effect of different process technology (40, 65, 90 nm) on our design, a novel design is implemented on 40, 65 and 90 nm based FPGA. In this work, we are integrating thermal aware design approach for energy efficient Vedic Multiplier on various FPGA using LVCMOS and HSTL I/O standard. LVCMOS is an acronym for Low Voltage Complementary Metal Oxide Semiconductor and HSTL is an acronym for High Speed Transceiver Logic. In this Vedic Multiplier, we are using three LVCMOS I/O standard and nine HSTL I/O standard. In order to test the thermal sustainability of our Vedic Multiplier design, we are testing it in three different room temperatures i.e. 20, 30, and 40 °C. Using LVCMOS25, there is 12.99, 19.23 and 10.28% reduction in power dissipation on 90, 65 and 40 nm FPGAs respectively. For LVCMOS25, when our Vedic Multiplier design is migrated from 40 nm design to 90 nm FPGA design, there is 87.72% reduction in power dissipation of Vedic Multiplier when temperature is kept constant at 20 °C. When temperature is scaled down from 50 to 20 °C there is 12.45, 14.93, 12.84, 9.45 and 8.48% saving in power dissipation on using HSTL_I, HSTL_I_12, HSTL_I_18, HSTL_I_DCI and HSTL_I_DCI_18 IO Standard respectively on 90 nm FPGA.

The Internet-of-Things (IoT) requires endnodes with ultra-low-power always-on capability for a long battery lifetime, as well as high performance, energy efficiency, and extreme flexibility to deal with complex and fast-evolving near-sensor analytics algorithms (NSAAs). We present Vega, an IoT endnode system on chip (SoC) capable of scaling from a 1.7- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu \text{W}$ </tex-math></inline-formula> fully retentive cognitive sleep mode up to 32.2-GOPS (at 49.4 mW) peak performance on NSAAs, including mobile deep neural network (DNN) inference, exploiting 1.6 MB of state-retentive SRAM, and 4 MB of non-volatile magnetoresistive random access memory (MRAM). To meet the performance and flexibility requirements of NSAAs, the SoC features ten RISC-V cores: one core for SoC and IO management and a nine-core cluster supporting multi-precision single instruction multiple data (SIMD) integer and floating-point (FP) computation. Vega achieves the state-of-the-art (SoA)-leading efficiency of 615 GOPS/W on 8-bit INT computation (boosted to 1.3 TOPS/W for 8-bit DNN inference with hardware acceleration). On FP computation, it achieves the SoA-leading efficiency of 79 and 129 GFLOPS/W on 32- and 16-bit FP, respectively. Two programmable machine learning (ML) accelerators boost energy efficiency in cognitive sleep and active states.

Energy consumption and efficiency technology measures in European non-residential buildings