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Artificial Intelligence for Edge Devices
  • AI Research Reports

Edge inference has emerged as a key workload in 2019–20, and many companies have introduced their chipsets. Several different factors are driving AI processing to the edge device. Privacy, security, cost, latency, and bandwidth are all being considered when evaluating data center versus edge processing needs. Applications like autonomous driving and navigation have sub-millisecond latency requirements that make edge processing mandatory. Other applications such as speech recognition on smart speakers generate privacy concerns. Keeping AI processing on the edge device circumvents privacy concerns while avoiding the bandwidth, latency, and cost concerns of cloud computing. Omdia forecasts that global AI edge chipset revenue will grow from $7.7bn in 2019 to $51.9bn by 2025.

This Omdia report provides a quantitative and qualitative assessment of the opportunity for AI edge processing across several consumer and enterprise device markets. The device categories include automotive, consumer and enterprise robots, drones, head-mounted displays (HMDs), mobile phones, PCs/tablets, security cameras, smart speakers, machine vision, and edge servers. Global revenue and shipment forecasts, segmented by chipset architecture, power consumption, compute capacity, training versus inference, and application attach rate for each device category, extend through 2025.

Deep Learning Chipsets
  • AI Research Reports

Deep learning (DL) is slowly moving past its hype cycle as proof-of-concept (PoC) AI applications developed in the past two years go into production. AI chipset customers have become more sophisticated in terms of chipset needs for AI application acceleration and are asking for specific benchmarks when talking to vendors. Customers’ needs for chipsets are coming to the forefront, forcing chipset companies to rethink the applicability of their technology. All prominent chip companies, such as Intel, NVIDIA, and Qualcomm, have invested heavily in AI. Cloud companies have started rolling out graphics processing units (GPUs), field-programmable gate arrays (FPGAs), and application-specific integrated circuits (ASICs), giving developers a choice for AI acceleration. Omdia forecasts that global revenue for DL chipsets will increase from $11.4bn in 2019 to $71.2bn by 2025.

This Omdia Market Report assesses the industry dynamics, technology issues, and market opportunity surrounding DL chipsets, including CPUs, GPUs, FPGAs, ASICs, and SoC accelerators. As an update to Omdia’s 2019 Deep Learning Chipsets report, it captures the state of this fast-moving chipset market. Global market forecasts, segmented by chipset type, compute capacity, power consumption, market sector, and training versus inference, extend through 2025. Omdia also provides profiles of 23 key industry players.

Processors for Graphics & Artificial Intelligence Market Tracker
  • AI Research Reports

Almost all suppliers of processor core technology are expected to integrate AI enhancements into their products in the coming years. This will trigger a major increase in AI support in SOCs, potentially bringing capabilities like voice recognition, face recognition and object recognition to billions of devices. Less than 5 percent of SOC devices now include AI capabilities. However, with the increasing integration of AI into processor cores, almost half of SOCs will be AI-capable by 2023.

This tracker provides an examination of the markets for processors targeting graphics, vision and AI acceleration functions including: Microprocessors with graphics and/or AI, Microcontrollers with graphics and/or AI, Graphics processors with or without AI, System-on-Chip and the emergence of AI-specific processors – ASIC and ASSP. Along with a database that includes 4-years of annual history and 5 year forecast, this tracker includes supplier market shares and deep analyses of verticals across 70 total applications.

Computer Vision Technologies and Markets
  • AI Research Reports
Computer vision (CV) has become commercialized in the past few years and is being deployed in a wide range of applications. The proofs-of-concepts that started a few years ago are now going into production. The rapid growth of technology, accurate results, falling price of hardware, and ease of connectivity are some of the factors enabling the rapid global adoption of CV. The global CV market has gained significant momentum in the past few years and does not show any sign of slowing down. Global CV market revenue is expected to grow across many use cases in different industries, increasing from $2.9bn in 2018 to $33.5bn by 2025. Omdia considers CV to be a horizontal market, as the applications are spread across many different use cases. This report focuses on 69 use cases in 25 industries that are tracked by Omdia.
Artificial Intelligence Business Models
  • AI Research Reports
The deployment of AI solutions is unlike traditional software, which is largely based around a volume-based sales model. In contrast, AI product capabilities may increase the output of employees, thus reducing the need for additional software license seats. Due to the collaborative and evolving nature of AI, several different business models have emerged. These range from a fully in-house, custom-built approach to a more modular approach using pre-built solutions and tools and a fully outsourced approach solely relying on third-party vendors. No single business model is going to be right for all enterprises looking to deploy AI. There will be room for many approaches and vendors—not only today, but for the foreseeable future. Omdia forecasts that annual AI software revenue will increase from $10.1bn worldwide in 2018 to $126.0bn in 2025.
Artificial Intelligence for Retail Applications
  • AI Research Reports
The retail industry has been facing major headwinds spawned by disruptors like Amazon, Alibaba, and Walmart, as well as hundreds of hungry startups, that have built lean, analytics-driven organizations based on scale and efficiency. The goal of these organizations is to drive top-line revenue and reduce operating costs. Overall, revenue and margins are under pressure as these more efficient and scalable disruptors draw more buyers and sales with sharper pricing, personalized customer journeys, and finely tuned assortments. Meanwhile, they are driving down costs through efficiencies in supply chain & inventory management.
Artificial Intelligence (AI) for medical diagnostics
  • AI Research Reports
It is clear that the application of AI within healthcare is here to stay. Part of the AI research Omdia been conducting over the last two years has been focused on machines developed primarily for medical diagnostics and drug discovery, with our most recent reporting now covering more than $8 billion in venture funding for 363 machines and counting, 28% of which now have regulatory approval (FDA, CE, MFDS, CFDA). This is up 15% from last year, and signals that the development community is pressing forward despite some lingering obstacles.
AI & Analytics in Video Surveillance Intelligence Service - Annual
  • AI Research Reports
Artificial intelligence and analytics are poised to revolutionize video surveillance by improving detection accuracy and by radically expediting processing and analysis of growing video footage volumes. This service helps you understand the competitive landscape and detect opportunities through complete coverage of global market trends and technologies-such as facial, behavior, vehicle and object recognition-shaping the video analytics market.
Cloud Robotics
  • AI Research Reports
The global cloud robotics market is at a nascent stage of development. As companies become aware of its growing importance, a gap remains in their understanding of what cloud robotics is, how it works, and what the implications are for their businesses. It is essentially the combination of cloud computing and robotics technologies in the form of hardware, software, and services. Cloud robotics is differentiated from general robotics through the use of teleoperation and cloud technologies. Another key differentiator is the emerging cloud-based robotics business model that enables connected robot as a service (RaaS), which allows for the more rapid deployment of adaptive robotic solutions. The benefits of cloud computing and cloud-based IT services also apply to cloud robotics. The same factors that are driving the growth of cloud technology and integration with the Internet of Things (IoT) and AI and the introduction of 5G connectivity are expected to stimulate strong growth in the cloud robotics market. This is why a number of major cloud and robotics companies are offering cloud robotics solutions for their customers. Although cloud robotics will not work for every situation, the rise of cloud computing, AI processing, and IoT are major driving forces for this market. Omdia expects global revenue for cloud robotics to increase from $5.3 billion in 2018 to $170.4 billion in 2025.
Quantum Computing for Enterprise Markets
  • AI Research Reports
Quantum computing (QC) can best be defined as the use of the attributes and principles of quantum mechanics to perform calculations and solve problems. Quantum computers are designed to utilize quantum bits (qubits), which are subatomic particles such as electrons and photons, to represent data. When these qubits are combined, or entangled, they can exist in multiple states (known as superposition). The result is that multiple calculations can be carried out at once, as each qubit can represent a value of 1, 0, or any point in between. A quantum system can process and analyze all data simultaneously and then return the best solution, along with thousands of close alternatives – all within microseconds. However, quantum computers are not destined to replace the processors in personal computers or smartphones anytime soon. For the most part, quantum computers will be best suited to addressing optimization problems, identifying patterns in data, and conducting complex simulations that would be too taxing for traditional, or classical, computers. These issues will drive the global market for enterprise QC. But quantum computers have not yet demonstrated quantum supremacy or quantum advantage. Significantly scaling the processing power, improving error correction abilities, and writing and refining quantum algorithms will be required before enterprises adopt QC en masse. Still, the QC market is expected to grow strongly through 2030. Omdia expects total enterprise QC market revenue to reach $9.1 billion annually by 2030, up from $111.6 million in 2018.


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