Siemens – Siemens invests in Skeleton Technologies expanding its data center portfolio

EMR Analysis

More information on Siemens AG: See full profile on EMR Executive Services

More information on Dr. Roland Busch (President and Chief Executive Officer, Siemens AG): See full profile on EMR Executive Services

More information on Veronika Bienert (Member of the Managing Board and Chief Financial Officer, Siemens AG): See full profile on EMR Executive Services

More information on “ONE Tech Company” Program by Siemens AG: See full profile on EMR Executive Services

 

 

More information on Siemens Smart Infrastructure (SI) by Siemens AG: See the full profile on EMR Executive Services

More information on Dr. Peter Körte (Member of the Managing Board and Chief Technology and Chief Strategy Officer with responsibility for Siemens Xcelerator and Siemens Advanta, Siemens AG + Member of the Managing Board and Chief Executive Officer, Siemens Smart Infrastructure (SI), Siemens AG): See the full profile on EMR Executive Services

More information on Markus Grabmeier (Chief Executive Officer, Electrical Products (EP) Business Unit, Siemens Smart Infrastructure (SI), Siemens AG): See the full profile on EMR Executive Services

 

 

 

More information on Skeleton Technologies (Strategic Investment and Exclusive Partnership by Siemens AG): https://www.skeletontech.com/ + Power systems for AI infrastructure

We develop rack-to-grid power systems engineered for the demands of next-generation AI compute infrastructure. 

Built on patented Curved Graphene technology and vertically integrated engineering, Skeleton power systems stabilize dynamic electrical loads from microseconds to minutes across mission-critical applications from AI to power grids.

Skeleton’s production locations in Germany are ideally located to service our customers globally. Our ISO9001, EMAS and ISO14001-certified Leipzig Superfactory in Germany is the largest and most modern supercapacitor factory globally.

Founded in 2009 in Estonia, we have raised almost 400 million euros of capital to scale up development and production of high-power systems for AI infrastructure and grid stability.

More information on Taavi Madiberk (Co-founder and Chief Executive Officer, Skeleton Technologies): https://www.skeletontech.com/about + https://www.linkedin.com/in/madiberk/ 

 

 

 

 

 

 

 

 

 

 

 

EMR Additional Notes:

• AI – Artificial Intelligence:
  • Artificial Intelligence (AI) is the broad field of computer science focused on developing systems that can perform tasks that typically require capabilities associated with human or other forms of intelligent behavior, such as learning, reasoning, perception, language understanding, planning, and decision-making.
  • AI systems may:
    • Process data or information from various sources;
    • Identify patterns, relationships, or relevant features;
    • Make predictions, classifications, decisions, or recommendations;
    • Reason, plan, generate content, or take actions toward defined goals.
  • AI is an umbrella term that includes machine learning, deep learning, generative AI, and other approaches such as rule-based systems, search, planning, knowledge representation, probabilistic methods, and optimization. Not all AI systems learn from data.
  • A popular but non-standard conceptual taxonomy describes AI as reactive machines, limited-memory systems, theory-of-mind AI, and self-aware AI. This taxonomy is useful for explaining different hypothetical levels of capability, but it is not a formal scientific classification of AI systems.
  • Main types of AI:
    • Type 1: Reactive machines. These AI systems do not use persistent memory of previous experiences to inform their decisions and generally respond only to the current input or state. An example is Deep Blue, the IBM chess-playing system that defeated Garry Kasparov in the 1990s. Deep Blue used chess-specific search and evaluation techniques to select moves rather than learning from previous games in the manner of modern machine-learning systems.
    • Type 2: Limited memory. These AI systems can use information from previous observations, stored data, or recent states when making decisions. Many modern AI systems use forms of memory or contextual information, although the term “limited memory” is a broad conceptual category rather than a precise technical classification.
    • Type 3: Theory of mind. A hypothetical form of AI capable of modeling aspects of other people’s beliefs, intentions, knowledge, emotions, or perspectives and using this understanding in interaction. Such capabilities remain an active area of research rather than an established category of deployed AI.
    • Type 4: Self-awareness. A hypothetical form of AI possessing a subjective sense of self or consciousness. No generally accepted evidence demonstrates the existence of genuinely self-aware or conscious AI systems.
  • AI programming therefore does not have a universally accepted set of “three cognitive skills”; learning, reasoning, perception, planning, language processing, and decision-making are among the capabilities commonly associated with AI.
  • Machine Learning (ML):
    • Machine Learning (ML) is a subset of AI in which algorithms learn patterns or relationships from data to perform tasks such as prediction, classification, generation, or decision-making, rather than relying solely on manually specified rules for every case.
    • ML uses historical or newly collected data to learn statistical patterns and improve performance on a defined task or objective.
    • ML is the dominant approach underlying many modern AI systems, but rule-based, optimization-based, search-based, and other non-ML approaches remain important in many applications.
    • ML allows software applications to learn from data and improve their performance on a defined task without requiring every decision rule to be explicitly programmed.
    • Recommendation engines are a common use case for ML. Other uses include fraud detection, spam filtering, image and speech recognition, forecasting, business process automation (BPA), and predictive maintenance.
    • Classical ML is often categorized according to how an algorithm learns from data. Common approaches include:
      • Supervised learning,
      • Unsupervised learning,
      • Semi-supervised learning,
      • Self-supervised learning, and
      • Reinforcement learning.
  • Deep Learning (DL):
    • Deep Learning (DL) is a subset of ML that uses neural networks with multiple computational layers to learn complex representations and relationships from data.
    • DL can be particularly effective for high-dimensional and unstructured or complex data such as images, audio, video, and natural language, although classical ML methods can outperform deep learning on some structured or tabular datasets.
    • DL uses multiple layers of information processing that can learn increasingly complex representations of input data. In some image-recognition systems, for example, earlier layers may learn lower-level visual features while later layers combine these features into more complex representations.
    • DL has enabled major advances in computer vision, speech recognition, natural language processing, generative AI, and other fields, but it is not inherently the “most sophisticated” AI architecture; different architectures and approaches are better suited to different tasks.
  • Generative AI (GenAI):
    • Generative AI (GenAI) refers to AI systems that generate new content or other outputs, such as text, images, code, audio, video, or structured data, based on patterns learned during training and on subsequent inputs or instructions.
    • GenAI is typically powered by machine-learning and deep-learning models, including large language models and other foundation models, rather than constituting a completely separate AI paradigm.
    • Generative AI can generate outputs based on prompts, structured inputs, multimodal inputs, tool calls, or instructions provided by people or software systems.
    • The public release of ChatGPT in November 2022 significantly increased public awareness and adoption of generative AI capable of producing natural-language responses and other forms of content.
  • Small Language Models (SLM) and Large Language Models (LLM):
    • Small Language Models (SLMs) are relatively compact language models designed to process and generate natural-language content, often with lower computational, memory, or deployment requirements than larger models.
    • Large Language Models (LLMs) are machine-learning models trained on large amounts of data to process and generate natural language and perform tasks such as text generation, summarization, translation, classification, question answering, reasoning, and information extraction.
    • “Small” and “large” are relative terms rather than universally defined categories based on a fixed number of parameters.
    • Modern LLMs are commonly based on transformer or related neural-network architectures.
    • Virtual assistants such as Siri, Alexa, and Google Assistant can incorporate language models, speech-recognition systems, search, rules, APIs, and other technologies; whether and to what extent they use LLMs depends on the specific version and implementation.
  • Computer Vision (CV) / Vision AI & Machine Vision (MV):
    • Computer Vision (CV) is a broad field of AI and computer science concerned with enabling computers to acquire, process, analyze, and interpret visual information from images, video, and other visual sensors.
    • CV can use deep learning, classical image-processing techniques, geometric methods, machine learning, and other computational approaches to recognize objects, detect patterns, estimate properties, understand scenes, or extract information from visual data.
    • Applications include object detection, image classification, optical character recognition (OCR), facial recognition, medical imaging, autonomous systems, visual search, and image-based measurement and inspection.
    • Machine Vision (MV) is an application of computer vision focused particularly on automated visual inspection, measurement, identification, guidance, and control, especially in industrial and manufacturing environments.
    • Machine Vision commonly uses cameras, lighting, image-processing software, AI or other vision algorithms, and interfaces to industrial control systems, robots, or other equipment.
    • The key difference is that CV is the broader technical field, while MV generally refers to application-oriented vision systems used to perform defined automated tasks.
  • Multimodal Intelligence and Agents:
    • Multimodal AI refers to AI systems that can process and/or generate information across multiple modalities, such as text, images, audio, video, and other sensor or structured data.
    • Multimodal capabilities allow AI systems to combine information from different types of input and output, enabling applications such as visual question answering, speech interaction, document understanding, and systems that can see, hear, speak, and generate visual or textual content.
    • An AI agent is a software system that perceives information from an environment, maintains or uses relevant state or context, and selects and takes actions toward a defined goal. An agent may incorporate one or more AI models, tools, memory, planning mechanisms, and orchestration components.
    • What distinguishes an AI agent from a conventional AI model is not simply decision-making, but the ability to take actions in an environment toward a goal, often through tools or external systems.
    • Agents can be classified or implemented in different ways, including reactive or planning-oriented agents, single-agent or multi-agent systems, and agents operating in static or dynamic environments. Multimodal agents can process multiple forms of information.
  • Agentic AI:
    • Agentic AI is an emerging term for AI systems designed to pursue defined goals by planning, making decisions, using tools, interacting with their environment, and taking actions with a degree of autonomy and limited human intervention.
    • Agentic AI systems may use large language models, other machine-learning models, planning mechanisms, memory or state, tools, external data sources, and orchestration components.
    • Unlike conventional AI models that primarily produce an output in response to an input, agentic systems extend model capabilities into action-oriented workflows, potentially executing multiple steps and adapting their actions based on intermediate results or changes in the environment.
    • Multi-agent systems are one implementation approach in which multiple specialized agents coordinate their activities to achieve a broader goal.
    • Agentic AI can build on generative AI, particularly LLMs, but agentic AI is not synonymous with generative AI or LLMs.
  • Agentic Enterprise:
    • An agentic enterprise is an emerging business operating model in which people, AI agents, enterprise applications, data, and automation systems work together to execute or coordinate business processes and workflows.
    • Compared with conventional automation, agentic systems can interpret information, make decisions within defined boundaries, use enterprise tools, and take actions across multiple workflow steps, while appropriate governance, security, permissions, monitoring, and human oversight can constrain or supervise those actions.
    • The term is increasingly used by technology companies and analysts to describe an evolution toward more autonomous AI-enabled enterprise operations, but it is not a standardized technical or organizational category.
  • Edge AI Technology:
    • Edge AI refers to the deployment and execution of AI models or inference workloads on or close to the devices and systems where data is generated, such as cameras, sensors, industrial controllers, vehicles, smartphones, and other IoT devices, rather than relying entirely on centralized cloud infrastructure.
    • Edge AI combines edge computing with AI/ML to process data locally or near its source, potentially reducing latency, bandwidth requirements, cloud dependence, or exposure of sensitive data.
    • Edge AI can operate with intermittent or no internet connectivity for some functions, although many systems continue to communicate with cloud or other centralized systems.
    • Applications include autonomous vehicles, industrial inspection, wearable devices, security cameras, robotics, and smart-home equipment.
  • High-Density AI:
    • High-density AI refers to the concentration of AI computing capacity—particularly GPUs or other accelerators, memory, networking, power delivery, and cooling—in a relatively small physical or data-center footprint.
    • It is an AI infrastructure and data-center design trend, not a distinct category of AI. High-density infrastructure can provide greater computing capacity per rack, room, or unit of floor space, but also creates significant requirements for power distribution, thermal management, networking, and physical infrastructure.
  • Explainable AI (XAI) and Human-Centered Explainable AI (HCXAI):
    • Explainable AI (XAI) refers to methods, techniques, and system designs intended to make AI model behavior, predictions, decisions, or outputs understandable to people.
    • Human-Centered Explainable AI (HCXAI) goes further by designing explanations around the needs, context, goals, knowledge, and capabilities of the people who use or are affected by the AI system.
    • While XAI focuses on understanding and communicating aspects of model behavior, HCXAI emphasizes whether explanations are relevant, comprehensible, useful, and appropriate for their human context. Related considerations can include fairness, accountability, trust, safety, and ethical use, although these are not themselves defining features of explainability.
  • Physical AI & Embodied AI:
    • Physical AI refers to AI systems designed to perceive, reason about, predict, and/or act in the physical world, often using data from sensors and controlling physical devices or actuators.
    • Embodied AI refers to AI systems whose intelligence is situated within or coupled to a physical or simulated body or agent, emphasizing the interaction between perception, decision-making, action, and the environment.
    • Physical AI and Embodied AI overlap substantially, and the terms are not universally defined as a strict hierarchy. They are commonly associated with robotics, autonomous vehicles, industrial systems, and other applications in which AI interacts with a physical environment.
  • Federated Learning and Reinforcement Learning:
    • Federated Learning (FL) is a machine-learning technique in which models are trained across multiple decentralized devices or organizations while the raw training data generally remains at its original location; model parameters, gradients, or other training information are communicated to an aggregation or coordination system. In simple terms: “Train AI without centrally collecting the raw data.”
    • Federated learning can improve data governance and reduce the need to centralize sensitive data, but it does not by itself guarantee privacy or security. Additional techniques such as secure aggregation or differential privacy may be used.
    • Reinforcement Learning (RL) is a type of machine learning in which an agent learns to select actions by interacting with an environment and receiving rewards or penalties, with the objective of improving its policy or long-term expected reward. In simple terms: “Learning through interaction and feedback.”
    • Federated Learning and Reinforcement Learning can be combined as Federated Reinforcement Learning (FRL), in which multiple agents or devices learn policies locally and share selected model information or updates to improve a common or coordinated policy without centrally sharing their raw experience data.
    • FRL is an active research area with applications being investigated in areas such as distributed resource management, communications networks, robotics, and autonomous systems.
  • AI Factories:
    • AI Factories are industrial-scale computing and operational infrastructures designed to transform data and computing resources into AI models, inference services, and other AI outputs at scale.
    • AI factories can encompass data preparation, model training, fine-tuning, inference, serving, storage, high-speed networking, accelerated computing, power, cooling, and associated software and operational systems.
    • “AI factory” is a conceptual and industry term rather than a standardized technical category. It can refer to a dedicated AI data center or, more broadly, an integrated infrastructure and operating model for producing and deploying AI capabilities at scale.
    • Companies and organizations use the term in different ways, including in connection with hyperscale and specialized data-center infrastructure, accelerated computing, and public-sector AI infrastructure initiatives.

 

 

 

• Capacitor – Supercapacitor – Ultracapacitor:
  • A Capacitor is a device for storing electrical energy in the form of an electric charge, consisting of two conductors in close proximity and insulated from each other. The space between the conductors may be filled by vacuum or with an insulating material known as a dielectric. Unlike the battery, a capacitor is a circuit component that temporarily stores electrical energy through distributing charged particles on (generally two) plates to create a potential difference. A capacitor can take a shorter time than a battery to charge up and it can release all the energy very quickly.
  • A supercapacitor, also called an ultracapacitor, is a high-capacity capacitor with a capacitance value much higher than other capacitors, but with lower voltage limits, that bridges the gap between electrolytic capacitors and rechargeable batteries.
  • Supercapacitors (SCs) are electrochemical energy storage devices that store and release energy by reversible adsorption and desorption of ions at the interfaces between electrode materials and electrolytes.
  • Supercapacitors are used in applications requiring many rapid charge/discharge cycles, rather than long-term compact energy storage — in automobiles, buses, trains, cranes and elevators, where they are used for regenerative braking, short-term energy storage, or burst-mode power delivery.
  • The supercapacitor is often misunderstood; it is not a battery replacement to store long-term energy. If, for example, the charge and discharge times are more than 60 seconds, use a battery; if shorter, then the supercapacitor becomes economical.
• Capacitor Bank:
  • A capacitor bank is a system consisting of several capacitors connected in series or parallel to form an energy storage system. This system helps in correcting the power factor lag and phase shift in an AC power supply, thus increasing the efficiency of electrical energy transfer. The primary purpose of a capacitor bank is to reduce the amount of electricity lost due to inductive reactance, which occurs when an alternating current (AC) passes through an inductor such as a transformer or motor.

 

 

 

• Grid, Microgrids, DERs and DERM’s:
  • Grid / Power Grid:
    • The power grid is a network for delivering electricity to consumers. The power grid includes power generation facilities, substations, transmission lines and towers, distribution networks, protection and control equipment, and associated communication and monitoring infrastructure.
      • The grid continuously balances electricity generation and consumption while maintaining system stability and power quality, supplying electricity for applications ranging from industry to household appliances.
      • Electric grids perform three major functions or stages of electricity supply: power generation, transmission, and distribution. Grid operation also includes functions such as system balancing, protection, control, monitoring, and, increasingly, energy storage and demand-side management.
  • Microgrid:
    • A microgrid is a group of interconnected loads and distributed energy resources (DERs) within clearly defined electrical boundaries that acts as a single controllable entity with respect to the main grid and can operate either connected to the main grid or, when appropriately designed, intentionally and controllably in an islanded mode.
      • Microgrids can integrate local generation, energy storage, controllable loads, and other DERs to improve resilience, flexibility, efficiency, or local energy management.
  • Smart Grid:
    • A smart grid is an electrical grid enhanced with digital communications, sensing, automation, control, and data/analytics technologies across generation, transmission, distribution, and/or customer-side systems to improve the monitoring, operation, efficiency, reliability, resilience, and flexibility of the power system.
  • Distributed Energy Resources (DERs): 
    • Distributed energy resources (DERs) are relatively small-scale electricity generation, storage, and flexible demand resources, located at or near the distribution system or customer premises, that can supply, store, or modify electricity consumption and are interconnected to the electric grid. They are often located close to load centers and can be used individually or in aggregate to provide value to the grid.
      • Common examples of DERs include rooftop solar PV units, small wind turbines, small gas-fired generators or engines/turbines, microturbines, biomass generators, fuel cells, combined heat and power (CHP) or tri-generation systems with electrical generation, battery storage, electric vehicles and controllable EV charging or discharging where they can provide grid flexibility, and demand response applications.
  • Distributed Energy Resources Management Systems (DERMS):
    • Distributed Energy Resources Management Systems (DERMS) are software platforms that help utilities, distribution system operators (DSOs), aggregators, and other energy-sector organizations monitor, provide visibility into, forecast, coordinate, optimize, and, where supported, control distributed energy resources (DERs).
      • DERMS can be used to aggregate and coordinate large numbers of DERs and flexible loads for grid services, including voltage and power-flow management, congestion and constraint management, balancing, flexibility services, resilience, and participation in demand-response or other electricity markets. DERMS can be defined in many ways, depending on the use case, the types of DERs being managed, the responsibilities of the organization using the system, and the architecture of the power system.

 

 

 

• Grounding and Bonding, Surge, Transient, Earth and Lightning Protection / Arresters Devices:
  • Grounding and Bonding:
    • Grounding (earthing in British English and many international standards contexts) is the intentional electrical connection of an electrical system, equipment, or conductive part to earth and/or to a grounding/earthing system, depending on the application. It establishes defined electrical reference and fault-current paths and contributes to limiting dangerous voltages and enabling protective devices to operate under fault conditions.
    • Bonding is the intentional electrical connection of exposed conductive parts, extraneous conductive parts, or other conductive elements to establish electrical continuity and reduce potential differences between them. This helps prevent dangerous touch voltages and provides low-impedance paths for fault and, where applicable, lightning currents.
    • Together, grounding and bonding contribute to a safe and controlled electrical environment by:
      • Providing low-impedance paths for fault and other intended currents, where required by the system design
      • Reducing dangerous potential differences between conductive parts
    • Equipotential bonding is a specific form of bonding that connects exposed conductive parts, extraneous conductive parts, and other relevant metallic elements so that potential differences between them are minimized, particularly during fault, lightning, or other transient conditions.
  • Surge:
    • A surge is a transient electrical phenomenon, typically involving an overvoltage and/or overcurrent, that can occur as a result of events such as lightning or switching operations. Its amplitude, duration, rise time, and waveform depend on the source and the electrical system.
    • Surge Protection Devices (SPDs), historically also referred to in some markets as Transient Voltage Surge Suppressors (TVSS), are devices designed to limit surge voltages and divert surge currents to protect electrical and electronic systems and equipment. IEC 61643-01:2024 specifies common requirements for low-voltage SPDs and describes them as devices containing at least one nonlinear component intended to limit surge voltages and divert surge currents.
    • An SPD does not normally create a short circuit during normal operation. Depending on its technology, it may become highly conductive during an overvoltage event or switch to a low-impedance path to divert current. The current path can be to earth/PE or between conductors, depending on the SPD configuration and system.
    • The “crowbar” function applies to switching-type protection devices and specific circuit-protection designs; it is not a characteristic of all SPDs.
  • Transient:
    • A transient is a short-duration, non-steady-state change in an electrical quantity such as voltage or current as a system moves between operating conditions. Transients can include transient overvoltages, transient overcurrents, oscillatory disturbances, and other short-duration phenomena.
    • Transient overvoltages may have very fast rise times and short durations, but there is no universal nanosecond-to-microsecond boundary that separates all transients from other electrical disturbances..
  • => Surge vs. Transient:
    • In practice, the terms are sometimes used interchangeably, particularly in the context of surge protection and transient overvoltages, but they are not best treated as two strictly separated time-duration categories.
    • Transient: a broader term describing a short-duration, non-steady-state electrical phenomenon.
    • Transient overvoltage: a transient involving a temporary increase in voltage.
    • Surge: commonly used for specific transient overvoltage and/or overcurrent phenomena, particularly those associated with lightning or switching.
    • Therefore, a surge can be a type of transient, while not every transient is necessarily called a surge.
  • Earthing and Lightning:
    • Earthing provides an intentional electrical connection to earth as part of an overall grounding/earthing system. Depending on the system design, this can involve ground/earth electrodes such as rods, tapes, meshes, plates, foundations, or buried conductors, together with bonding and protective conductors.
    • Lightning protection is a coordinated system of measures designed to reduce the risk of physical damage to structures and injury to people, and to reduce damage to electrical and electronic systems caused by lightning. It can include:
      • air-termination systems
      • down-conductors
      • earth-termination systems
      • equipotential bonding
      • surge protection devices (SPDs) and other measures for protecting internal electrical and electronic systems
    • Lightning protection is not only about directing lightning current to earth. It also involves controlling current paths, minimizing dangerous potential differences, managing separation distances, and reducing touch and step voltages and induced/conducted lightning effects. IEC 62305-1 and IEC 62305-3 cover these broader protection principles, including protection against injury from touch and step voltages.

 

 

 

• Cloud Computing:
  • Cloud computing is a general term for anything that involves delivering hosted services over the internet. It is the on-demand availability of computer system resources, especially data storage and computing power, without direct active management by the user. Large clouds often have functions distributed over multiple locations, each location being a data center. Cloud services typically include IaaS, PaaS, and SaaS service models.
• Edge Computing:
  • Edge computing is a form of computing that is done on site or near a particular data source, minimizing the need for data to be processed in a remote data center.
  • Edge computing can enable more effective city traffic management. Examples of this include optimising bus frequency given fluctuations in demand, managing the opening and closing of extra lanes, and, in future, managing autonomous car flows.
  • An edge device is any piece of hardware that controls data flow at the boundary between two networks. Edge devices fulfill a variety of roles, depending on what type of device they are, but they essentially serve as network entry — or exit — points.
  • There are five main types of edge computing devices: IoT sensors, smart cameras, uCPE equipment, servers and processors. IoT sensors, smart cameras and uCPE equipment will reside on the customer premises, whereas servers and processors will reside in an edge computing data centre.
  • In service-based industries such as the finance and e-commerce sector, edge computing devices also have roles to play. In this case, a smart phone, laptop, or tablet becomes the edge computing device.
  • Edge Devices:
    • Edge devices encompass a broad range of device types, including sensors, actuators and other endpoints, as well as IoT gateways. Within a local area network (LAN), switches in the access layer — that is, those connecting end-user devices to the aggregation layer — are sometimes called edge switches.
    • Edge devices act as the interface between the physical world (data generation) and digital networks.

 

• Hybrid Computing: 
  • A hybrid cloud integrates private, on-premises infrastructure with public cloud services, offering flexibility to distribute workloads between these environments. Hybrid models often incorporate edge computing, allowing organizations to run critical workloads locally at the edge while using the cloud for other tasks, thereby optimizing performance, cost, and data management for various business needs.
• HPC (Hight-Performance Computing):
  • Practice of aggregating computing resources to gain performance greater than that of a single workstation, server, or computer. HPC can take the form of custom-built supercomputers or groups of individual computers called clusters.
  • HPC is typically used for simulation, scientific computing, AI training, and complex modeling.
• Data Centers – Physical Infrastructure:
  • A data center is a facility that centralizes an organization’s shared IT operations and equipment for the purposes of storing, processing, and disseminating data and applications. Because they house an organization’s most critical and proprietary assets, data centers are vital to the continuity of daily operations.
• Hyperscale Data Centers – Physical Infrastructure:
  • The clue is in the name: hyperscale data centers are massive facilities built by companies with vast data processing and storage needs. These firms may derive their income directly from the applications or websites the equipment supports, or sell technology management services to third parties.
  • Hyperscale Data Centers are typically operated by large cloud providers (e.g., hyperscalers) and designed for horizontal scalability.
• White Space and Grey Space in Data Centers – Physical Infrastructure:
  • White space in a data center refers to the area where IT equipment is placed. It typically houses servers, storage, network gear, and racks.
  • Gray space, on the other hand, is the area where the back-end infrastructure is located. This space is essential for supporting the IT equipment and includes areas for switchgear, UPS, transformers, chillers, and generators.
• Colocation in Data Centers – Physical Infrastructure:
  • A colocation data center is a facility where businesses rent space, power, and cooling to house their own servers and networking hardware, rather than maintaining them in-house. It offers a cost-effective way to access high-level security, internet connectivity, and 24/7 technical support while retaining control of the equipment.
• Edge & Cloud Services – Integrated Architecture (Edge-to-Cloud): 
  • Edge services perform data processing on local devices and servers near the data source, reducing latency for time-sensitive operations, while cloud services centralize large computations and storage in remote datacenters, offering massive scalability and flexibility for general workloads.
  • Most organizations use both, creating an “edge-to-cloud” architecture where edge devices handle immediate tasks, and the cloud manages large-scale data processing and complex applications, providing a seamless and efficient experience.

 

 

 

• Power Utility – Utilities:
  • A power utility, also known as an electric utility, is an organization responsible for providing electricity-related services through one or more activities in the electricity supply chain. These activities may include electricity generation, transmission, distribution and retail supply to residential, commercial and industrial customers. The scope of a utility’s responsibilities depends on the structure of the electricity market and the applicable regulatory framework.
  • In vertically integrated electricity markets, a single utility may perform several or all of these functions. In liberalized or unbundled markets, they are often carried out by separate organizations, such as generation companies, transmission system operators (TSOs), distribution system operators (DSOs) and electricity retailers. Transmission and distribution networks are generally regulated because they exhibit natural-monopoly characteristics, while electricity generation and retail supply may be open to competition.
  • Power utilities and other electricity-sector organizations play an essential role in maintaining a reliable electricity supply. Their responsibilities may include operating generation facilities or networks, maintaining infrastructure, managing capacity and supporting system reliability. Depending on the market structure, responsibility for grid stability, balancing and continuity of supply may be shared among utilities, system operators, regulators and other market participants. Electricity supply cannot be guaranteed without interruption, as outages and system disturbances can occur.

 

 

 

• Power Swing:
  • A power swing is an oscillation in electrical power flow and associated power-system quantities that occurs when the relative rotor angles and speeds of synchronous generators change following a disturbance in an interconnected power system. These oscillations can be triggered by events such as short-circuit faults and their subsequent clearance, transmission-line switching, or the loss of a generator or significant load.
  • During a power swing, active power flow and other electrical quantities, such as voltage, current and reactive power, may vary as the system responds to the disturbance. A power swing may be stable, when the generators maintain synchronism and the oscillations eventually subside, or unstable, when rotor-angle differences continue to increase and synchronism is lost.
  • Power swings are important in power-system protection because the changing apparent impedance seen by distance protection relays can resemble a fault condition. Power-swing detection and blocking functions help prevent unwanted relay tripping during stable swings, while out-of-step protection may be used to initiate appropriate action when the system loses synchronism.

 

 

 

• Chip, Computer Chip and Integrated Circuit (IC):
  • An integrated circuit (IC) is an electronic circuit in which electronic components such as transistors, diodes, resistors, capacitors, and their interconnections are fabricated together in or on a semiconductor substrate, typically silicon. ICs can contain from a few components to billions of transistors and are manufactured on semiconductor wafers, which are subsequently separated into individual dies.
  • Terms are often used interchangeably but there are subtle differences:
    • Chip: Is the most general informal term. It commonly refers to a semiconductor die or an integrated-circuit device, although the exact meaning depends on context. A chip can contain an IC and may subsequently be packaged for use in an electronic system.
    • Integrated Circuit (IC): This is the technical term for an electronic circuit whose components and interconnections are fabricated together as an integrated structure, typically on a semiconductor substrate. It describes the electronic functionality implemented in the integrated circuit, rather than simply the physical piece of semiconductor material.
    • Die: A die is an individual piece of semiconductor material containing an IC or other semiconductor device, separated from a processed wafer during semiconductor manufacturing. A die is commonly packaged before being used as a finished semiconductor component.
    • Computer Chip: This term is an informal, broad term for a semiconductor device or IC used in a computer or computing system, such as a microprocessor/CPU, memory device, graphics processor/GPU, or other processor or accelerator.
• AI Chips:
  • Artificial intelligence (AI) chips are semiconductor processors or accelerators optimized for the computational workloads used by AI and machine-learning systems. They are designed to efficiently perform operations such as matrix and vector computations, tensor operations, and other highly parallel workloads used in neural-network training and inference. AI chips can support applications including generative AI, computer vision, speech processing, recommendation systems, and natural-language processing (NLP).
  • AI chips can be based on different architectures and technologies, including GPUs, CPUs with AI acceleration, NPUs, TPUs, ASICs, FPGAs, and other specialized accelerators. They are not necessarily a single standardized category of semiconductor device.
  • Chips are made primarily from semiconductor materials, with silicon being the dominant substrate material for mainstream integrated circuits. Other semiconductor materials, including compound and wide-bandgap materials, are used for specific applications.
  • Taiwan is a major global center for semiconductor manufacturing, including advanced semiconductor fabrication, with Taiwan Semiconductor Manufacturing Company (TSMC) being one of the world’s leading semiconductor foundries. The geographic distribution of semiconductor manufacturing and the relative share of advanced-node production are dynamic market characteristics.
  • NVIDIA uses external semiconductor foundries, including TSMC, to manufacture many of its advanced processors and accelerators. The specific manufacturing arrangements and process nodes used for individual NVIDIA products vary by generation and product.
• Grid-to-Chip:
  • “Grid-to-chip” is an expression used mainly in data-center, power-delivery, and AI-infrastructure contexts.
  • It describes the end-to-end electrical power-delivery chain from the utility/grid connection to the semiconductor device, including the intermediate electrical distribution and power-conversion stages required to deliver usable power to computing equipment and ultimately to the chip. Depending on the architecture, these stages can include transformers, switchgear, generators, UPS systems, power distribution equipment, power supplies, voltage regulators, and point-of-load or on-package power-conversion stages.
  • The term emphasizes the complete power path and the increasing importance of power delivery and conversion as computing and AI power densities increase.
• System-On-a-Chip:
  • A System-on-a-Chip (SoC) is an integrated circuit that combines multiple major functional blocks of a computer or electronic system onto a single semiconductor chip. Depending on its application, an SoC may integrate components such as one or more CPUs, GPUs or other accelerators, memory controllers, interfaces, security functions, communication/connectivity blocks, and peripheral controllers.
  • Rather than implementing the system using separate ICs for each major function, an SoC integrates many of these functions into a single chip, reducing physical size, inter-chip communication, power consumption, and potentially system cost.
  • External components may still be required, such as memory, storage, power-management components, sensors, antennas, or other specialized devices, depending on the system.

 

 

 

• Fundamental Electrical Quantities and Units:
  • Electric Current:
    • Ampere (A): The ampere is the SI unit of electric current. Electric current represents the rate of flow of electric charge through a system. Depending on the medium, charge may be carried by electrons, ions, holes or other charge carriers.
      • 1 A = 1 C/s
      • In the current SI, the ampere is defined by fixing the elementary charge at exactly 1.602 176 634 × 10⁻¹⁹ C.
    • Milliampere (mA): The milliampere is a unit of electric current equal to one-thousandth of an ampere.
      • 1 mA = 10⁻³ A
      • It is commonly used for small currents in electronic circuits, sensors, control systems and low-power devices.
    • Kiloampere (kA): The kiloampere is a unit of electric current equal to 1,000 amperes.
      • 1 kA = 10³ A
      • It is commonly used to express high electrical currents, such as short-circuit currents, busbar ratings and current ratings of large electrical equipment.
  • Electrical Potential Difference (Voltage):
    • Volt (V): The volt is the SI unit of electric potential difference, commonly called voltage. Voltage represents the difference in electric potential energy per unit electric charge between two points:
      • 1 V = 1 J/C
      • A potential difference can produce an electric current when an appropriate conductive path and circuit conditions exist.
    • Kilovolt (kV): The kilovolt is a unit of electric potential difference equal to 1,000 volts:
      • 1 kV = 10³ V
  • Electrical Power and Energy:
    • Power: Power is the rate at which energy is transferred, converted or delivered. The SI unit of power is the watt (W):
      • 1 W = 1 J/s
      • Electrical power may be expressed in W, kW, MW, GW or TW.
    • Energy: Energy is the amount of energy transferred, converted or stored. Electrical energy is commonly expressed in joules (J) or watt-hours (Wh), including kWh, MWh and GWh.
      • For constant power:
    • Energy = Power × Time
      • For variable power, energy is obtained by integrating power over time.
      • Electrical energy consumption is commonly billed in kWh, although electricity tariffs may also include charges based on demand or capacity.
  • Real or Active Power:
    • Real power (P), also called active power, is the average rate of net energy transfer in an electrical system. It represents the portion of electrical power associated with net transfer or conversion of energy, such as mechanical work, heat or light. It is expressed in watts (W) and its multiples.
    • Kilowatt (kW): 1 kW = 1,000 W.
    • Megawatt (MW): 1 MW = 1,000 kW = 1,000,000 W.
    • Gigawatt (GW): 1 GW = 1,000 MW = 1,000,000,000 W.
    • Terawatt (TW): 1 TW = 1,000 GW = 10¹² W. It is used for very large power levels, such as aggregated national or global generating capacity or instantaneous electrical demand. Energy consumed or generated over time should instead be expressed in units such as TWh.
  • Apparent and Reactive Power:
    • Apparent power (S) is a measure of the total RMS voltage-current loading of an AC system and is expressed in volt-amperes (VA). For sinusoidal AC:
      • S = V × I
      • Apparent power encompasses both active and reactive power. The apparent-power rating of equipment such as transformers, generators and UPS systems is commonly specified in VA, kVA or MVA.
    • Kilovolt-ampere (kVA): 1 kVA = 1,000 VA.
    • Megavolt-ampere (MVA): 1 MVA = 1,000 kVA = 1,000,000 VA.
    • Reactive power (Q) is the component of AC power associated with the periodic exchange of energy between the electrical system and reactive elements such as inductors and capacitors. It is expressed in var (volt-amperes reactive), kVAr or MVAr.
      • For sinusoidal AC: S² = P² + Q²
      • and:
      • P = S × power factor
      • Thus, at a power factor below unity, apparent power is greater than active power.
  • Specialized Renewable Energy Unit:
    • Kilowatt-peak (kWp):
      • kWp is a conventional designation for the rated peak power of a photovoltaic module or system under specified reference test conditions. It is a power rating, not a unit of energy. For conventional crystalline-silicon PV modules, Standard Test Conditions (STC) use an irradiance of 1,000 W/m², a cell temperature of 25°C and a specified reference solar spectrum.
      • For example, four 270 W modules have a combined nominal peak rating of: 4 × 270 W = 1.08 kWp
      • Actual electricity production depends on solar irradiance, orientation, tilt, shading, temperature, system losses, degradation and other operating conditions. Therefore, a kWp rating does not imply a fixed annual energy yield in kWh.

 

 

 

• AC (Alternating Current) & DC (Direct Current) & UC (Universal Current):
  • Direct Current (DC):
    • Electric current that is unidirectional, meaning the flow of charge is always in the same direction. Unlike alternating current, the direction does not change. It is used in many household electronics and in all battery-powered devices.
    • Direct current has many uses, from charging batteries to supplying power for electronic systems, motors, and industrial processes. Very large quantities of DC power are used in applications such as aluminum smelting and other electrochemical processes.
    • DC is more efficient for long-distance transmission at very high voltages (HVDC) because it avoids reactive power losses and reduces skin effect and capacitive losses, especially over long distances and submarine cables.
  • Alternating Current (AC): 
    • Alternating current is an electric current in which the direction of flow periodically reverses (typically 50 or 60 Hz).
    • AC is used in power grids and homes because it can be easily transformed to higher or lower voltages using transformers. This allows efficient transmission at high voltage over long distances and safe distribution at low voltage for end users.
    • DC can also be converted to different voltage levels, but it requires power electronics (converters), not simple transformers.
  • Universal Current (UC): .
    • Universal Current (UC) means a device can operate with either AC or DC input.
    • For example, a 24 V UC input can accept either 24 V AC or 24 V DC.
    • UC is not a type of current, but a device input specification indicating compatibility with both AC and DC supplies.

 

 

• Volts of Direct Current (VDC):
  • VDC stands for Volts of Direct Current, representing electric potential in a system where current flows consistently in one direction, from positive to negative. Unlike VAC (Alternating Current), VDC is used by batteries, solar panels, and electronics, providing stable, non-reversing power, typically for low-voltage devices, electronics, and industrial sensors.
• VDC Main Distribution Bus:
  • A VDC (Volts Direct Current) main distribution bus is a central, heavy-duty electrical conductor—typically a copper or aluminum bar—used to collect and distribute direct current power from sources (like batteries, solar panels, or rectifiers) to various loads in a high-power system. It acts as the central backbone of a DC power architecture, commonly operating at higher voltages (e.g., 380V, 400V, 800V, or 1000V) in modern industrial, data center, and marine applications to increase efficiency and reduce copper losses.