inference on the edge

Extracted Attributes

• Benefit

  Enhanced data security by processing data locally.

• Definition

  The capability of artificial intelligence (AI) to perform complex computations and decision-making directly on local devices, rather than relying on centralized cloud servers.

• Applications

  Real-time AI applications in industries such as gaming, healthcare, retail, image recognition, fraud detection, chatbots, virtual try-on services, smart manufacturing, smart cities, defense, 5G telecommunications, robotics, and drones.

• Primary Benefit

  Significantly reduces the energy consumption associated with AI, enabling more powerful AI applications.

• Related Concepts

  Edge AI, Edge computing, Machine Learning (ML) inference.

• Alternative Definition

  The process of running trained machine learning models on unseen data directly on or near the device itself, such as a GPU-powered server at a factory or an IoT device in a vehicle, retail store, private home, smartphone, or sensor.

• Enabling Technologies/Hardware

  FPGA accelerator cards, SAKURA SoCs, smartphones, sensors, IoT devices.

Timeline

• An architectural proposal from Germany was highlighted for its aim to drastically reduce energy consumption for AI, potentially enabling more powerful inference on the edge. (Source: related_documents)

  Undated (recent)

Bayesian inference

Bayesian inference ( BAY-zee-ən or BAY-zhən) is a method of statistical inference in which Bayes' theorem is used to calculate a probability of a hypothesis, given prior evidence, and update it as more information becomes available. Fundamentally, Bayesian inference uses a prior distribution to estimate posterior probabilities. Bayesian inference is an important technique in statistics, and especially in mathematical statistics. Bayesian updating is particularly important in the dynamic analysis of a sequence of data. Bayesian inference has found application in a wide range of activities, including science, engineering, philosophy, medicine, sport, and law. In the philosophy of decision theory, Bayesian inference is closely related to subjective probability, often called "Bayesian probability".

Web Search Results

• Edge AI Inference: Use Cases And Guide - Mirantis

  ## What Is AI Inference at the Edge? AI inference is the process of running trained machine learning models on unseen data. Increasingly, inference is occurring at the edge, even though model training typically takes place in centralized environments like data centers or public clouds. [...] AI inference at the edge enables real-time execution directly on or near the device itself, whether that’s a GPU-powered server at a factory or an IoT device in a vehicle, retail store, private home, or other location. Inference at the edge increases speed and helps organizations scale workloads efficiently by cutting down on latency and reducing dependence on central cloud resources. [...] Balancing cloud and edge infrastructure can help increase performance while reducing operational expenses. Edge inference carries out tasks locally, which reduces the strain on bandwidth and central resources. This, in turn, cuts down on costs.

• What is AI inference at the edge, and why is it important ... - TechRadar

  AI inference at the edge refers to running trained machine learning (ML) models closer to end users when compared to traditional cloud AI inference. Edge inference accelerates the response time of ML models, enabling real-time AI applications in industries such as gaming, healthcare, and retail. ## What is AI inference at the edge? [...] AI inference at the edge is a subset of AI inference whereby an ML model runs on a server close to end users; for example, in the same region or even the same city. This proximity reduces latency to milliseconds for faster model response, which is beneficial for real-time applications like image recognition, fraud detection, or gaming map generation. Latest Videos From TechRadar Seva Vayner Product Director of Edge Cloud and AI at Gcore. ## How AI inference at the edge relates to edge AI [...] For organizations looking to deploy real-time applications, AI inference at the edge is an essential component of their infrastructure. It significantly reduces latency, ensuring ultra-fast response times. For end users, this means a seamless, more engaging experience, whether playing online games, using chatbots, or shopping online with a virtual try-on service. Enhanced data security means businesses can offer superior AI services while protecting user data. AI inference at the edge is a

• AI inference in edge computing: Benefits and use cases

  As artificial intelligence (AI) continues to evolve, its deployment has expanded beyond cloud computing into edge devices, bringing transformative advantages to various industries. AI inference at the edge computing refers to the process of running trained AI models directly on local hardware, such as smartphones, sensors, and IoT devices, rather than relying on remote cloud servers for data processing. [...] AI inference at the edge enables a high degree of customization and personalization by processing data locally, allowing systems to deploy customized models for individual user needs and specific environmental contexts in real-time. [...] However, centralizing this data for processing can result in delays and privacy issues. This is where edge AI inference becomes crucial. By integrating intelligence directly into the smart sensors, AI models facilitate immediate analysis and decision-making right at the source.

• What is edge AI inference doing for more devices? - EdgeCortix

  Low latency: Unlike most cloud-based applications, latency is usually a primary concern at the edge. Edge AI inference has a fixed window for performing recognition against a real-time data stream bound by sample or frame rates or risk falling behind and missing changes. Inference decisions may also feed control algorithms in a deterministic window with limited time to respond and stay in control. [...] With the help of ecosystem partners, many possible implementations of edge AI inference are possible. An FPGA accelerator card can host a flexible implementation when more space and power are available, such as in a smart manufacturing or smart city application. A SAKURA SoC can deliver inference in a smaller package ready for a custom board design if size and weight are concerns, like in defense, 5G telecommunications, or robotics and drone applications. More customization is also an option, [...] By accounting for all these parameters, neural network IP can go farther at the edge. Less power consumption translates to better battery life for more range and extended use. Determinism paves the way for real-time applications. And, by packing more inferences per second per watt into a given space, an edge device can take on more complex AI models and deliver features not possible with less efficient approaches. ### Edge AI inference in more form factors for more applications

• Moving ML Inference from the Cloud to the Edge - Jo Kristian Bergum

  In this blog post, I investigate running Machine Learned (ML) inference computation as close as possible to where the model input data is generated on a user's device at the edge. There are several definitions of "Edge," but for me, Edge computations cover inference computations on the user device, in the Browser, and compute at the front of Cloud regions in Content Delivery Networks (CDNs). Examples of computing infrastructure on the Edge are Lambda@Edge from AWS and Cloudflare Workers from [...] In this blog post, I will cover a Holiday project of mine where the goal was to run ML Inference in the Browser with an NLP model for text classification. ## Motivation for Inference at the Edge There are, as I see it, three primary reasons for running ML inference on the user device instead of in the Cloud: [...] ## Building blocks for Edge ML Inference To be able to perform real-time inference on the device, there are several technologies or building blocks needed: