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Traditionally, AI inference, also referred to as AI processing, has been centralized in large data centers, where vast computational resources are available. However, there is increasing demand for real-time applications and immediate responses.
This demand has led to the rise of an AI model that distributes computation and data processing across multiple devices.
This approach is known as distributed AI (DAI) and designed to handle large-scale computations and data by using the collective power of distributed systems, such as clusters, networks or edge devices.
By leveraging the benefits of both cloud and edge computing to meet rising workload demands at the edge, distributed AI will push the boundaries of what artificial intelligence can achieve.
Join Micron as we uncover how distributed AI works and what benefits it brings. Contact our sales support team for further information.
What is distributed AI?
Distributed AI definition: Distributed AI is a computing approach where data processing is centralized for security while AI inference tasks are decentralized and executed across multiple nodes or devices — often in parallel — to improve efficiency, scalability and responsiveness.
Traditional centralized AI systems concentrate data processing in a single unit. But DAI systems bypass the need to move vast amounts of data to the single unit. Instead, they execute AI algorithms across multiple cloud and devices, including data analysis at the source.
While distributed AI centralizes data processing but distributes inference workloads across cloud and edge devices, edge AI processes data locally on edge devices. Both distributed AI and edge AI offer significant benefits in terms of security and privacy, albeit through different approaches. Essentially, distributed AI systems use the collective intelligence of interconnected devices for collaborative learning and problem-solving.
Because DAI centralizes data processing, sensitive information is handled in this centralized location. This centralization allows organizations to implement advanced privacy-enhancing technologies — such as differential privacy and homomorphic encryption — which protect data during processing. Additionally, by distributing inference tasks and not having to move large amounts of data, DAI minimizes exposure during transmission.
On the other hand, edge AI processes data locally on edge devices, reducing the need to transmit data to centralized servers. This local processing minimizes exposure to potential breaches during data transmission, thereby enhancing security and privacy. By understanding these distinct methods, we can appreciate how both DAI and edge AI contribute to a more secure and privacy-conscious AI ecosystem.
As we have established, distributed AI seamlessly integrates AI inference with the cloud and edge, enhancing processing efficiency and reducing response times. These improvements lead to better user experiences and more scalable AI applications by mitigating the latency, power and bandwidth challenges that come with sending data to data centers. Mitigating these challenges also requires more from devices themselves.
Overall, distributed AI represents significant progress in AI technology and signals the direction of future developments. Enabling devices to infer and process data in real time — on the devices themselves — is much more powerful and dynamic than previous iterations.
How does distributed AI work?
Distributed AI uses both cloud and edge processing to provide more efficient and scalable AI solutions. In this system, AI training and development typically occur in the cloud, where computational resources are abundant.
Once trained, models are then deployed to edge devices for real-time inference, enabling faster responses and reducing the need for constant connectivity. This approach not only enhances the efficiency and scalability of AI systems, but it also ensures that AI capabilities are accessible and effective in diverse environments.
The separation of training, development, and deployment is strategic. AI devices are designed with rules that dictate when processing should occur internally and when processing should be sent to the cloud. This distinction optimizes resource use, allowing devices to handle lightweight tasks internally while offloading more demanding workloads to the cloud.
The key to designing powerful and efficient DAI systems lies in dynamically recognizing system resource constraints and distributing processing workload accordingly. By integrating agentic AI, which leverages the capabilities of autonomous agents, and using small language models (SLMs) for edge devices, distributed AI systems can unlock the true potential of artificial intelligence.
DAI models vary in their specific functionality rules, influenced by several key factors that determine how workloads are distributed:
- The type of device: Devices with low internal computational power can handle simpler datasets themselves but need to offload more demanding processing tasks.
- The type of data: Datasets differ, leading to different processing requirements. A model needing to process large, complex datasets — with a mixture of textual, numeric and graphic data — likely has to offload this larger workload to the cloud.
- Connectivity between the device and cloud: Distributed AI relies on quick communication between a device and a cloud location. This reliance means that the connection between the device and cloud must be secure and efficient, allowing for high-speed data transfer between the two points.
What is the history of distributed AI?
- Mid-1970s, introduction of distributed AI: DAI emerged as a subfield of AI in the mid-1970s to address the limitations of centralized AI systems, which struggled with scalability and robustness. Early DAI systems were designed to operate independently, allowing for greater flexibility and adaptability in dynamic environments.
- 1990s, advancements in machine learning: The 1990s marked a pivotal era for DAI, with significant advancements in machine learning and natural language processing (NLP). Researchers explored new techniques such as decision trees and neural networks, enabling machines to learn from data and improve performance over time. Despite challenges like limited computing power, this decade saw the rise of expert systems and the development of chatbots like A.L.I.C.E., which aimed to pass the Turing test. The period also witnessed the AI winter, where funding and interest in AI research temporarily declined.
- Early 2000s, artificial intelligence drive: The early 2000s brought a shift toward data-driven AI, using massive datasets for improved performance. This era saw the rise of big data AI, during which capabilities in image recognition, speech recognition and personalized news feeds were enhanced. Distributed AI systems became more robust and adaptive, and they integrated multi-agent systems and distributed problem-solving approaches. The increased availability of data and advancements in computational power significantly boosted the development and application of DAI.
- 2020s, breakthroughs in distributed AI: The 2020s have been characterized by unprecedented advancements in AI, driven by breakthroughs in deep learning, natural language processing, and generative AI models like GPT-3 and GPT-4. These advancements have allowed DAI systems to use vast amounts of data and computational power, leading to more sophisticated and scalable solutions. The integration of AI into various industries — from healthcare to finance — has demonstrated the transformative potential of DAI in solving complex, real-world problems.
The rapid pace of innovation continues to push the boundaries of what is possible with distributed AI, making it a critical component of modern AI research and applications.
What are key types of distributed AI?
Key types of distributed AI include device-centric, device-sensing, and joint-processing distributed AI.
- Device-centric distributed AI anchors the device itself as the core processing location, with only a small number of tasks offloaded to the cloud when the device lacks the computational power or processing capability to achieve sufficient outputs.
This approach ensures that most of the data processing occurs locally on the device, reducing the need for constant cloud connectivity and enhancing real-time responsiveness.
An example of this type is AI-powered chatbots on laptops. In this case, the device handles most of the processing locally, providing quick and efficient responses without relying heavily on cloud resources. - Device-sensing distributed AI models process the raw input data to turn it into textual data before sending it to the cloud for further processing. This method allows initial data preprocessing to be handled locally, an approach that can reduce the amount of data to be transmitted to the cloud and improve overall system efficiency.
Sensors in smart home devices exemplify this type of distributed AI, as they preprocess data locally before sending it to the cloud for more complex analysis and decision-making. - Joint-processing distributed AI splits the processing workload between the device and the cloud to maximize outputs, using multiple large language models (LLMs) to process distinct parts of a dataset individually.
This approach leverages the strengths of both local and cloud processing to ensure that tasks are handled by the most appropriate resource.
Autonomous vehicles are a prime example of joint-processing distributed AI. They process data locally to make immediate driving decisions while offloading more complex tasks, such as route optimization and traffic analysis, to cloud servers.
How is distributed AI used?
One major use case for distributed AI is embedding more powerful AI models within mobile devices like smartphones and tablets. AI-powered chatbots like ChatGPT are disrupting the tech industry, including the evolution of mobile devices and search functions. The rising popularity of these chat-style search engines relies on the growing power and developing sophistication of DAI, processing between the local device and LLMs within the cloud.
Another use case for DAI is the evolution of autonomous vehicles. Technology that was once science fiction, driverless cars and AVs are quickly becoming a reality thanks to the intervention of distributed AI. In these vehicles, DAI models allow the complex data processing to be offloaded to cloud computers while the core data collection and refinement remains local.
Distributed AI is already embedded in our everyday lives, thanks to the internet of things (IoT). AI models are used to improve decision-making within the technologies that make up the IoT network, driving efficiencies and enhancing user experience across a range of industries.
Distributed AI involves multiple autonomous agents working together to solve complex problems. These agents can operate independently but communicate by integrating partial solutions into a complete one.
Distributed learning involves splitting the training of machine learning models across multiple nodes to handle large datasets efficiently. It is commonly used in scenarios requiring fast training on big data, such as recommendation systems and real-time analytics.
In summary, distributed AI involves multiple agents working together to solve problems, while distributed learning involves distributing the training process of machine learning models across multiple nodes to handle large datasets efficiently.
Distributed AI focuses on decentralization and collaborative learning, with multiple autonomous agents working together across various nodes or devices to solve complex problems. The output comprises the collective intelligence of these interconnected devices. This approach involves both AI processing and AI inference, as each agent processes data locally and collaboratively infers solutions to complex problems.
In contrast, hybrid AI combines on-device processing with cloud-based processing to optimize performance, personalization, privacy, and security. This approach balances local AI processing for immediate tasks with cloud-based AI inference for more complex decision-making.
While both approaches use on-device and cloud processing, distributed AI emphasizes decentralization and collaborative learning whereas hybrid AI aims to balance local and cloud processing to achieve optimal results.
AI processing encompasses all the computational tasks involved in developing, training and deploying AI models, including data collection, preprocessing, model training, and evaluation.
It is a resource-intensive process aimed at teaching AI to learn from data. In contrast, AI inference is the stage where a trained AI model is used to make predictions or decisions based on new, unseen data. This stage involves applying the model to generate outputs, often requiring quick and efficient real-time decision-making.
While AI processing focuses on learning and accuracy, AI inference emphasizes speed and application. Both are critical to AI systems but serve distinct roles and cannot be used interchangeably. Micron Technology excels in both areas, ensuring robust and efficient AI solutions.
Distributed AI centralizes data processing for enhanced security while decentralizing inference tasks across multiple nodes or devices to improve efficiency and scalability. In contrast, edge AI processes data locally on edge devices, minimizing data transmission to enhance security and privacy by reducing exposure to potential breaches.