LONDON, August 14, 2018 /PRNewswire/ -- CPUs, GPUs, FPGAs, ASICs, SoC Accelerators, and Other Chipsets for Training and Inference Applications: Global Market Analysis and Forecasts
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Artificial intelligence (AI) technology is progressing at a rapid pace, as is the application of the technology to solve real-world problems. While the market for chipsets to address deep learning training and inference workloads is still a new one, the landscape is changing quickly – in the past year, more than 60 companies of all sizes have announced some sort of deep learning chipset or intellectual property (IP) design. Every prominent name in the technology industry has acknowledged the need for hardware acceleration of AI algorithms and the semiconductor industry has responded by offering a wide range of solutions.
The deep learning chipset market has experienced a dynamic period of evolution during the past year and promises to become even more interesting. Beginning in 2018, many companies will start releasing their new chipsets, after which the market validation will then begin. Tractica expects that 2019 and 2020 will be the years when a ramp-up in deep learning chipset volumes will take place and winners will begin to emerge. Tractica forecasts that the market for deep learning chipsets will increase from $1.6 billion in 2017 to $66.3 billion by 2025. The edge computing market, where AI computation is done on the device, is expected to represent more than three-quarters of the total market opportunity, with the balance being in cloud/data center environments. Mobile phones will be a major driver of the edge market, and other prominent edge categories include automotive, smart cameras, robots, and drones.
This Tractica report assesses the industry dynamics, technology issues, and market opportunity surrounding deep learning chipsets including CPUs, GPUs, FPGAs, ASICs, SoC Accelerators, and other chipsets. The report provides market sizing and forecasts for the period from 2016 through 2025, with segmentation by chipset type, compute capacity, power consumption, world region, and inference versus training. The study also includes 19 profiles of key industry players.
Key Questions Addressed:
- What is the mix of chipset types being used for deep learning today, and how will it change during the next 10 years?
- Which chipset types are most appropriate for training versus inference applications?
- What will be the power consumption and compute capacity profiles of chipsets used for various deep learning applications?
- What is the market opportunity for deep learning chipsets in cloud/data center environments versus edge devices?
- Which market sectors and industries will drive demand for deep learning chipsets?
- What is the state of technology development for deep learning chipsets, and who are the key industry players driving innovation?
Who Needs This Report?
- Semiconductor and component manufacturers
- Service providers and systems integrators
- End-user organizations deploying deep learning systems
- Industry associations
- Government agencies
- Investor community
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