NEW YORK, Sept. 4, 2018 /PRNewswire/ — Edge-Based AI Chipsets and Accelerators for Mobile Phones, Smart Speakers, Head-Mounted Displays, Automotive, PCs/Tablets, Drones, Security Cameras, and Robots: Global Market Analysis and Forecasts

Read the full report: https://www.reportlinker.com/p05494577 

Artificial intelligence (AI) processing today is mostly done in a cloud-based data center. The majority of AI processing is dominated by training of deep learning models, which requires heavy compute capacity. In the last 6 years, we have seen a 300,000X growth in compute requirements, with graphics processing units (GPUs) providing most of that horsep ower. AI inference, which is performed post-training, and is relatively less compute intensive, has been largely overlooked from an AI processing standpoint. Like training, inference has also been largely done in the data center. However, as the diversity of AI applications grows, the centralized, cloud-based training and inference regime is coming into question.

AI edge processing today is focused on moving the inference part of the AI workflow to the device, keeping data constrained to the device. There are several different reasons why AI processing is moving to the edge device, depending on the application. Privacy, security, cost, latency, and bandwidth all need to be considered when evaluating cloud versus edge processing. The impact of model compression techniques like Google’s Learn2Compress that enables squeezing large AI models into small hardware form factors is also contributing to the rise of AI edge processing. Federated learning and blockchain-based decentralized AI architectures are also part of the shift of AI processing to the edge with part of the training also likely to move to the edge. Depending on the AI application and device category, there are several hardware options for performing AI edge processing. These options include CPUs, GPUs, ASICs, FPGAs, and SoC accelerators.

This Tractica report provides a quantitative and qualitative assessment of the market 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, mobile phones, PCs/tablets, security cameras, and smart speakers. The report includes segmentation by processor type, power consumption, compute capacity, and training versus inference for each device category, with unit shipment and revenue forecasts for the period from 2017 to 2025.

Key Questions Addressed:

• What are the reasons for AI processing moving to the edge?
• How will AI processing hardware vary among different device types?
• What is the role of SoC accelerators at the edge and how will they compare to ASICs, CPUs, GPUs, and FPGAs?
• What will be the attach rates for AI edge processing across different device categories between 2017 and 2025?
• How will 5G networks impact the deployment of AI edge processors?
• Which hardware companies will benefit from AI processing moving to the edge?
• What is the impact of China and Chinese hardware companies on the AI edge processing market?

Who Needs This Report?

• Artificial intelligence technology companies
• Semiconductor and component vendors
• Edge computing software vendors
• Electronics manufacturers
• Automotive companies
• Drone and robot manufacturers
• Investor community

Read the full report: https://www.reportlinker.com/p05494577

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