The NPS3102A and NPS3102B eFuses have a wide input voltage range (21 V abs. max) and have an integrated low-resistance pass MOSFET which minimizes voltage drop and power loss. This feature helps to increase operating efficiency beyond that attainable from similar competing devices in the same package type. The current clamp limit can be adjusted in the 2–13.5 A range using a resistor at the ILIM pin, which can also be used to measure load current in real-time. Both devices include a built-in overvoltage clamp that limits the output voltage during an input overvoltage condition, with a 2 μs short-circuit protection response time.

The pass-FET in the NPS3102A must be manually reset after a fault event whereas the NPS3102B integrates an auto-retry block, which safely attempts to re-enable the pass-FET without the need for user intervention. Apart from having the ability to quickly protect systems against input and output overvoltages and large inrush currents, Nexperia eFuses are designed to protect the downstream loads from high current fault conditions given its 10% current limiting accuracy. The NPS3102A and NPS3102B are available in DFN3030-10/SOT8037-1 leadless plastic packages measuring only 3.0 x 3.0 x 0.75 mm.

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The International Telecommunication Union has established a Focus Group on Environmental Efficiency for Artificial Intelligence and other Emerging Technologies and issued a draft report, ITU-T FG-AI4EE D.WG2-02: Computer processing, data management and energy perspective. This FAQ provides an overview of the energy savings in 5G networks that can be enabled by artificial intelligence (AI) and machine learning (ML), looks at specific uses for AI energy savings in base stations, and closes by looking at energy savings enabled by AI in RANs.

AI-based energy-saving algorithms can be implemented with no impact on network performance and no large-scale equipment deployments or network hardware changes. A large amount of equipment in a typical base station runs in idle mode for much of the time. That makes AI-enabled dynamic shutdowns one of several important energy savings tools. According to Nokia, benefits of AI energy management for mobile networks can include:

• Up to 30% overall energy savings
• Up to 5 times more power savings than non-AI systems that perform temporary shutdowns based on fixed schedules
• Up to 70% less energy consumption for cooling.=
• Minimization of energy waste across active radio and auxiliary equipment

Dynamic shutdown is one of the most significant energy savings opportunities for AI in 5G. AI can handle complex cross-domain optimization problems and develop dynamic strategies. Networks generally have cyclical use patterns, for example, peaking during the workday and shrinking dramatically in the middle of the night, and using a fixed equipment shutdown can save energy. But those patterns aren’t absolute. Using AI enables network operators to develop predictive analysis based on parameter optimization at the application level, combined with ML training and optimization for model building and data cleaning and aggregation at the data processing level, leading to optimized data acquisition and proactive and improved configuration management instead of shutdowns on a fixed schedule (Figure 1).

AI and RANs
RANs experience wide swings in traffic based on where they are located and the time. Efficiently managing the operation of various cells and shutting down unnecessary capacity is critical to minimizing RAN energy consumption. Not every cell in a RAN experiences the same loading pattern, and they need individualized energy management in real time. That’s where AI and ML come in to enable dynamic configuration changes across the cells to maintain a high quality of service (QoS) while reducing energy consumption by up to 12%. One possible approach to using AI and ML is shown in Figure 2:

1. The high-level solution consists of an energy consumption forecasting model.
2. The methodology is the tool used to develop the optimal configuration threshold grid search model.
3. Model Fitting identifies the preferred threshold validation on the network.
4. Optimized Scenario performs an impact analysis to adjust the model as needed to support the required QoS.

Two key models in this approach are the forecasting and optimization models. The forecasting model works a day ahead to predict energy consumption for each cell. It provides the baseline for the optimization model, which identifies potential improvement in terms of performance criteria like accessibility, throughput, anticipated traffic volume, mobility, latency, and so on. AI and ML are used here to implement and refine a convex optimization model to maximize sleep hours subject to business and technical/performance constraints.

Summary
AI and ML provide powerful tools for energy management in 5G systems. The ITU has active programs to identify and develop AI-based tools to develop holistic approaches to data processing, data management, and energy management. Those tools can be used to develop energy-saving algorithms for individual cells and RANs.

References
Application of AI technology in 5G base station to Improve Energy efficiency, China Telecom
Focus Group on Environmental Efficiency for Artificial Intelligence and other Emerging Technologies (FG-AI4EE), International Telecommunication Union
How Artificial Intelligence reduces the carbon footprint of telco networks, Nokia
ITU-T FG-AI4EE D.WG2-02: Computer processing, data management and energy perspective, International Telecommunication Union
Optimizing energy efficiency with AI-powered energy management software, Nokia
Why AI-powered RAN is an energy efficiency breakthrough, Ericsson

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Up to 69 % smaller than other solutions, the regulator modules released today each offer two high-performance MOSFETs, an inductor, and a controller, with only minimal external components needed for configuration and loop compensation. The devices’ compact size dramatically increases power density, while their high level of integration reduces design complexity and time to market. The regulators’ controllers consume minimum quiescent current, enabling peak efficiencies of up to 97 %. In data centers, the telecom infrastructure, and industrial applications, microBRICK regulators help reduce energy consumption by more efficiently delivering power to FPGAs, ASICs, and SoC core power supplies.

Highly configurable, the regulators combine their wide input voltage ranges with adjustable output voltages down to 0.3 V. In addition, the SiC931 features four programmable switching frequencies at 600 kHz, 1 MHz, 1.5 MHz, and 2 MHz, while the SiC967 and SiC951 offer adjustable switching ranges from 100 kHz to 2 MHz and 300 kHz to 1.5 MHz, respectively. All three devices offer an adjustable current limit, while the SiC931 features an adjustable soft start and the PMBus 1.3 compliant SiC951 supports sequential, tracking, and simultaneous operation.

The regulator modules provide versatile solutions for a wide range of applications, including POL converters in servers, cloud computing, high-performance computing, and desktop computers; industrial automation, motor drives, and tools; surveillance systems; consumer electronics; and 5G telecom equipment. For these applications, the SiC951 and SiC967 offer three operating modes: forced continuous conduction, ultrasonic, and power save. The SiC931 provides forced continuous conduction and power-saving modes. In power-saving mode, when the inductor current crosses zero, the control scheme turns off the low-side MOSFET to deploy a diode emulation mode. The switching frequency decreases in proportion to load conditions. There is no minimum switching frequency limitation, allowing for the best possible efficiency at light loads.

The devices’ constant on-time (COT) architecture delivers ultrafast transient response with minimum output capacitance and tight ripple regulation at very light loads. It also enables loop stability regardless of the type of output capacitor used, including low ESR ceramic capacitors. The regulators feature a robust protection feature set for reliable operation, output overvoltage (OVP) and under voltage protection (UVP), cycle-by-cycle overcurrent protection (OCP), short circuit protection (SCP) with auto-retry, over temperature protection (OTP), and a power good flag.

Samples and production quantities of the new regulator modules are available now, with lead times of 20 weeks. Pricing for U.S. delivery in 1000-piece quantities is $8.50 for the SiC931, $9 for the SiC967, and $10 for the SiC951.

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