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Add new TikZ figures in chapter 12
- Added new TikZ figures in chapter 12 - Modified the code for introductory chapter figures (chapters 11–20) - Fixed syllable splitting for words that were overflowing text boxes
This commit is contained in:
Zeljko Hrcek
@@ -107,4 +107,5 @@ precommit
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boxS
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Handlin
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Dota
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ALine
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ALine
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TE
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@@ -5,7 +5,14 @@ quiz: ai_for_good_quizzes.json
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# AI for Good
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::: {.column-margin}
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_DALL·E 3 Prompt: Illustration of planet Earth wrapped in shimmering neural networks, with diverse humans and AI robots working together on various projects like planting trees, cleaning the oceans, and developing sustainable energy solutions. The positive and hopeful atmosphere represents a united effort to create a better future._
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:::
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\noindent
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:::
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## Purpose {.unnumbered}
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@@ -4,7 +4,14 @@ bibliography: conclusion.bib
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# Conclusion
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::: {.column-margin}
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_DALL·E 3 Prompt: An image depicting the last chapter of an ML systems book, open to a two-page spread. The pages summarize key concepts such as neural networks, model architectures, hardware acceleration, and MLOps. One page features a diagram of a neural network and different model architectures, while the other page shows illustrations of hardware components for acceleration and MLOps workflows. The background includes subtle elements like circuit patterns and data points to reinforce the technological theme. The colors are professional and clean, with an emphasis on clarity and understanding._
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:::
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\noindent
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:::
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## Overview {#sec-conclusion-overview-fcb4}
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@@ -5,7 +5,15 @@ quiz: hw_acceleration_quizzes.json
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# AI Acceleration
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::: {layout-narrow}
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::: {.column-margin}
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_DALL·E 3 Prompt: Create an intricate and colorful representation of a System on Chip (SoC) design in a rectangular format. Showcase a variety of specialized machine learning accelerators and chiplets, all integrated into the processor. Provide a detailed view inside the chip, highlighting the rapid movement of electrons. Each accelerator and chiplet should be designed to interact with neural network neurons, layers, and activations, emphasizing their processing speed. Depict the neural networks as a network of interconnected nodes, with vibrant data streams flowing between the accelerator pieces, showcasing the enhanced computation speed._
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:::
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\noindent
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:::
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## Purpose {.unnumbered}
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@@ -3279,20 +3287,19 @@ library(ggrepel) # Smart label positioning with lines
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# Data for processors, extended to 2030 (without WSE-4)
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processors <- data.frame(
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year = c(1971, 1974, 1978, 1982, 1985, 1989, 1993, 1997, 1999, 2000,
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2006, 2008, 2012, 2016, 2017, 2018, 2019, 2020, 2021, 2021, 2022, 2023, 2024),
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year = c(1971, 1974, 1978, 1982, 1985, 1989, 1993, 1997, 1999, 2000,2006, 2008,
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2012, 2016, 2017, 2018, 2019, 2020, 2021, 2021, 2022, 2023, 2024),
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processor = c("Intel 4004", "Intel 8080", "Intel 8086", "Intel 80286", "Intel 80386",
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"Intel 80486", "Intel Pentium", "Intel Pentium II", "Intel Pentium III",
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"Intel Pentium 4", "Intel Core 2 Duo", "Intel Core i7",
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"NVIDIA Tesla K20", "NVIDIA Tesla P100", "Google TPU v2",
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"NVIDIA Tesla V100", "Cerebras WSE-1", "NVIDIA A100",
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"Cerebras WSE-2", "Google TPU v4", "NVIDIA H100", "Apple M2 Ultra", "Cerebras WSE-3"),
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transistors = c(0.0023, 0.006, 0.029, 0.134, 0.275, 1.2, 3.1, 7.5, 9.5, 42,
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291, 731, 7100, 15000, 4500, 21100, 1200000, 54200,
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transistors = c(0.0023, 0.006, 0.029, 0.134, 0.275, 1.2, 3.1, 7.5, 9.5, 42,291, 731,
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7100, 15000, 4500, 21100, 1200000, 54200,
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2600000, 100000, 80000, 134000, 4000000),
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category = c("CPU", "CPU", "CPU", "CPU", "CPU", "CPU", "CPU", "CPU", "CPU", "CPU",
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"CPU", "CPU", "GPU", "GPU", "TPU", "GPU", "WSE", "GPU",
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"WSE", "TPU", "GPU", "CPU", "WSE")
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category = c("CPU", "CPU", "CPU", "CPU", "CPU", "CPU", "CPU", "CPU", "CPU", "CPU","CPU", "CPU",
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"GPU", "GPU", "TPU", "GPU", "WSE", "GPU","WSE", "TPU", "GPU", "CPU", "WSE")
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)
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# Define muted colors for each category
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@@ -5,7 +5,15 @@ quiz: ondevice_learning_quizzes.json
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# On-Device Learning
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::: {layout-narrow}
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::: {.column-margin}
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_DALL·E 3 Prompt: Drawing of a smartphone with its internal components exposed, revealing diverse miniature engineers of different genders and skin tones actively working on the ML model. The engineers, including men, women, and non-binary individuals, are tuning parameters, repairing connections, and enhancing the network on the fly. Data flows into the ML model, being processed in real-time, and generating output inferences._
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:::
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\noindent
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:::
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## Purpose {.unnumbered}
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@@ -94,7 +102,7 @@ Each of these domains highlights a common pattern: the deployment environment in
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Most machine learning systems today follow a centralized learning paradigm. Models are trained in data centers using large-scale, curated datasets aggregated from many sources. Once trained, these models are deployed to client devices in a static form, where they perform inference without further modification. Updates to model parameters, either to incorporate new data or to improve generalization, are handled periodically through offline retraining, often using newly collected or labeled data sent back from the field.
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This centralized model of learning offers numerous advantages: high-performance computing infrastructure, access to diverse data distributions, and robust debugging and validation pipelines. However, it also depends on reliable data transfer, trust in data custodianship, and infrastructure capable of managing global updates across a fleet of devices. As machine learning is deployed into increasingly diverse and distributed environments, the limitations of this approach become more apparent.
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This centralized model of learning offers numerous advantages: high-per­for­`mance computing infrastructure, access to diverse data distributions, and robust debugging and validation pipelines. However, it also depends on reliable data transfer, trust in data custodianship, and infrastructure capable of managing global updates across a fleet of devices. As machine learning is deployed into increasingly diverse and distributed environments, the limitations of this approach become more apparent.
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In contrast, on-device learning is inherently decentralized. Each device maintains its own copy of a model and adapts it locally using data that is typically unavailable to centralized infrastructure. Training occurs on-device, often asynchronously and under varying resource conditions. Data never leaves the device, reducing exposure but also complicating coordination. Devices may differ substantially in their hardware capabilities, runtime environments, and patterns of use, making the learning process heterogeneous and difficult to standardize.
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@@ -5,7 +5,15 @@ quiz: ops_quizzes.json
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# ML Operations
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::: {layout-narrow}
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_DALL·E 3 Prompt: Create a detailed, wide rectangular illustration of an AI workflow. The image should showcase the process across six stages, with a flow from left to right: 1. Data collection, with diverse individuals of different genders and descents using a variety of devices like laptops, smartphones, and sensors to gather data. 2. Data processing, displaying a data center with active servers and databases with glowing lights. 3. Model training, represented by a computer screen with code, neural network diagrams, and progress indicators. 4. Model evaluation, featuring people examining data analytics on large monitors. 5. Deployment, where the AI is integrated into robotics, mobile apps, and industrial equipment. 6. Monitoring, showing professionals tracking AI performance metrics on dashboards to check for accuracy and concept drift over time. Each stage should be distinctly marked and the style should be clean, sleek, and modern with a dynamic and informative color scheme._
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:::
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\noindent
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:::
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## Purpose {.unnumbered}
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@@ -1988,7 +1996,6 @@ In each case, operational maturity is not an external constraint but an architec
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::: {#fig-uptime-iceberg fig-env="figure" fig-pos="htb"}
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```{.tikz}
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\begin{tikzpicture}[line join=round,font=\usefont{T1}{phv}{m}{n}\small]
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\tikzset{Line/.style={line width=1.5pt,BlueD},
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mysnake/.style={postaction={line width=2.5pt,BlueD,draw,decorate,
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decoration={snake,amplitude=1.8pt,segment length=18pt}}},
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@@ -2366,7 +2373,7 @@ This feedback loop positions the clinician not merely as a prescriber but as a c
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#### Hypertension Case Example {#sec-ml-operations-hypertension-case-example-ae5f}
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To concretize the principles of ClinAIOps, consider the management of hypertension—a condition affecting nearly half of adults in the United States (48.1%, or approximately 119.9 million individuals, according to the Centers for Disease Control and Prevention). Effective hypertension control often requires individualized, ongoing adjustments to therapy, making it an ideal candidate for continuous therapeutic monitoring.
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To concretize the principles of ClinAIOps, consider the management of hyper­ten­sion—a condition affecting nearly half of adults in the United States (48.1%, or approximately 119.9 million individuals, according to the Centers for Disease Control and Prevention). Effective hypertension control often requires individualized, ongoing adjustments to therapy, making it an ideal candidate for continuous therapeutic monitoring.
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ClinAIOps offers a structured framework for managing hypertension by integrating wearable sensing technologies, AI-driven recommendations, and clinician oversight into a cohesive feedback system. In this context, wearable devices equipped with photoplethysmography (PPG) and electrocardiography (ECG) sensors passively capture cardiovascular data, which can be analyzed in near-real-time to inform treatment adjustments. These inputs are augmented by behavioral data (e.g., physical activity) and medication adherence logs, forming the basis for an adaptive and responsive treatment regimen.
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@@ -5,7 +5,15 @@ quiz: privacy_security_quizzes.json
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# Security & Privacy
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::: {layout-narrow}
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::: {.column-margin}
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_DALL·E 3 Prompt: An illustration on privacy and security in machine learning systems. The image shows a digital landscape with a network of interconnected nodes and data streams, symbolizing machine learning algorithms. In the foreground, there's a large lock superimposed over the network, representing privacy and security. The lock is semi-transparent, allowing the underlying network to be partially visible. The background features binary code and digital encryption symbols, emphasizing the theme of cybersecurity. The color scheme is a mix of blues, greens, and grays, suggesting a high-tech, digital environment._
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\noindent
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:::
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## Purpose {.unnumbered}
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@@ -5,7 +5,15 @@ quiz: responsible_ai_quizzes.json
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# Responsible AI
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::: {layout-narrow}
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_DALL·E 3 Prompt: Illustration of responsible AI in a futuristic setting with the universe in the backdrop: A human hand or hands nurturing a seedling that grows into an AI tree, symbolizing a neural network. The tree has digital branches and leaves, resembling a neural network, to represent the interconnected nature of AI. The background depicts a future universe where humans and animals with general intelligence collaborate harmoniously. The scene captures the initial nurturing of the AI as a seedling, emphasizing the ethical development of AI technology in harmony with humanity and the universe._
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\noindent
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:::
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## Purpose {.unnumbered}
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@@ -5,7 +5,15 @@ quiz: robust_ai_quizzes.json
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# Robust AI
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::: {layout-narrow}
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_DALL·E 3 Prompt: Create an image featuring an advanced AI system symbolized by an intricate, glowing neural network, deeply nested within a series of progressively larger and more fortified shields. Each shield layer represents a layer of defense, showcasing the system's robustness against external threats and internal errors. The neural network, at the heart of this fortress of shields, radiates with connections that signify the AI's capacity for learning and adaptation. This visual metaphor emphasizes not only the technological sophistication of the AI but also its resilience and security, set against the backdrop of a state-of-the-art, secure server room filled with the latest in technological advancements. The image aims to convey the concept of ultimate protection and resilience in the field of artificial intelligence._
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\noindent
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:::
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## Purpose {.unnumbered}
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@@ -5,7 +5,14 @@ quiz: sustainable_ai_quizzes.json
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# Sustainable AI
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::: {.column-margin}
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_DALL·E 3 Prompt: 3D illustration on a light background of a sustainable AI network interconnected with a myriad of eco-friendly energy sources. The AI actively manages and optimizes its energy from sources like solar arrays, wind turbines, and hydro dams, emphasizing power efficiency and performance. Deep neural networks spread throughout, receiving energy from these sustainable resources._
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:::
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\noindent
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:::
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## Purpose {.unnumbered}
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@@ -620,7 +627,7 @@ To mitigate these risks, fabs must continue advancing green chemistry initiative
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While silicon is abundant and readily available, the fabrication of AI accelerators, GPUs, and specialized AI chips depends on scarce and geopolitically sensitive materials that are far more difficult to source. AI hardware manufacturing requires a range of rare metals, noble gases, and semiconductor compounds, many of which face supply constraints, geopolitical risks, and environmental extraction costs. As AI models become larger and more computationally intensive, the demand for these materials continues to rise, raising concerns about long-term availability and sustainability.
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Although silicon forms the foundation of semiconductor devices, high-performance AI chips depend on rare elements such as gallium, indium, and arsenic, which are essential for high-speed, low-power electronic components [@chen2006gallium]. Gallium and indium, for example, are widely used in compound semiconductors, particularly for 5G communications, optoelectronics, and AI accelerators. The United States Geological Survey (USGS) has classified indium as a critical material, with global supplies expected to last fewer than 15 years at the current rate of consumption [@davies2011endangered].
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Although silicon forms the foundation of semiconductor devices, high-per­for­mance AI chips depend on rare elements such as gallium, indium, and arsenic, which are essential for high-speed, low-power electronic components [@chen2006gallium]. Gallium and indium, for example, are widely used in compound semiconductors, particularly for 5G communications, optoelectronics, and AI accelerators. The United States Geological Survey (USGS) has classified indium as a critical material, with global supplies expected to last fewer than 15 years at the current rate of consumption [@davies2011endangered].
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Another major concern is helium, a noble gas critical for semiconductor cooling, plasma etching, and EUV lithography[^fn-euv] used in next-generation chip production. Helium is unique in that once released into the atmosphere, it escapes Earth's gravity and is lost forever, making it a non-renewable resource [@davies2011endangered]. The semiconductor industry is one of the largest consumers of helium, and supply shortages have already led to price spikes and disruptions in fabrication processes. As AI hardware manufacturing scales, the demand for helium will continue to grow, necessitating more sustainable extraction and recycling practices.
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@@ -892,7 +899,7 @@ The design phase sets the foundation for the entire AI lifecycle, influencing en
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### Manufacturing Phase {#sec-sustainable-ai-manufacturing-phase-c775}
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The manufacturing phase of AI systems is one of the most resource-intensive aspects of their lifecycle, involving the fabrication of specialized semiconductor hardware such as GPUs, TPUs, FPGAs, and other AI accelerators. The production of these chips requires large-scale industrial processes, including raw material extraction, wafer fabrication, lithography, doping, and packaging—all of which contribute significantly to environmental impact [@@nakano2021geopolitics]. This phase not only involves high energy consumption but also generates hazardous waste, relies on scarce materials, and has long-term consequences for resource depletion.
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The manufacturing phase of AI systems is one of the most resource-intensive aspects of their lifecycle, involving the fabrication of specialized semiconductor hardware such as GPUs, TPUs, FPGAs, and other AI accelerators. The production of these chips requires large-scale industrial processes, including raw material extraction, wafer fabrication, lithography, doping, and packaging—all of which contribute significantly to environmental impact [@nakano2021geopolitics]. This phase not only involves high energy consumption but also generates hazardous waste, relies on scarce materials, and has long-term consequences for resource depletion.
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#### Fabrication Materials {#sec-sustainable-ai-fabrication-materials-c298}
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@@ -125,6 +125,13 @@ singlelinecheck=false,width=\linewidth,labelsep=none,font={ninept}}
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prin-ci-ples
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ex-per-tise
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com-pli-cat-ed
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blue-print
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per‧for‧mance
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com-mu-ni-ca-tion
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par-a-digms
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hy-per-ten-sion
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per-for-mance
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a-chieved
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}
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\lstset{
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