Crowded AI Chip Market Still Has Room for New Entrants According to New Linley Group Study

Deep-learning technology gains traction in nearly every industry.

MOUNTAIN VIEW, Calif.–(BUSINESS WIRE)–lt;a href=”” target=”_blank”gt;#AIlt;/agt;–Artificial intelligence (AI) processors have quickly become an essential
technology for accelerating deep-learning applications in data centers,
automotive, client devices, embedded (IoT) systems, and other areas. A
new report from The Linley Group, “A
Guide to Processors for Deep Learning
,” analyzes deep-learning
accelerators and IP cores for artificial intelligence, neural networks,
and vision processing for inference and training. Many new companies and
products target this fast-growing market, which topped $4 billion in
chip revenue in 2018.

The rapid adoption of deep-learning applications has spurred the
development of AI chips and IP to meet the performance needs of complex
neural networks. Because no single processor can satisfy such a broad
range of applications, some vendors are developing diverse sets of
products while others are specializing in custom architectures to
provide superior performance and efficiency for specific workloads.
These custom architectures are now starting to challenge the common use
of CPUs and GPUs for AI. The report analyzes these architectures and
products to determine which will win over time.

“With the advent of deep neural networks, we’ve seen AI processing sweep
through the tech sector and spill over into many industries around the
world,” said Linley Gwennap, principal analyst with The Linley Group.
“Although it may appear that the landscape of AI chip and IP vendors is
getting crowded, there are still opportunities for new suppliers to gain
traction provided they have a superior product.”

The report provides detailed technical coverage of announced
deep-learning accelerator chips from AMD, Google, Graphcore, Gyrfalcon,
Intel (including Xeon, Stratix, Mobileye, and Movidius), Microsoft,
Mythic, Nvidia (including Tesla and AGX), NXP, Qualcomm, Wave Computing,
and Xilinx. It also covers deep-learning IP cores from AImotive, Arm,
Cadence, Cambricon, Ceva, Imagination, Synopsys, and Videantis as well
as the open-source NVDLA. Briefly covered are deep-learning accelerators
from Amazon, Bitmain, BrainChip, Cerebras, Cornami, eSilicon, Eta
Compute, General Processor, GreenWaves, Groq, Habana, Huami, Huawei,
NovuMind, and SambaNova.

The report includes extensive technical and market overviews to help
those coming up to speed on this complex technology. Those seeking a
quantitative look at the market for deep-learning accelerators will find
market size and forecasts in three market segments: data center and HPC,
ADAS and autonomous vehicles, and client and IoT.


“A Guide to Processors for Deep Learning” is available now directly from
The Linley Group. For further details, including pricing, visit the web
site at

About The Linley Group

The Linley Group is the industry’s leading source for independent
technology analysis of semiconductors for networking, communications,
mobile, and data-center applications. The company provides strategic
consulting services, in-depth analytical reports, and conferences
focused on advanced technologies for chip and system design. The Linley
Group also publishes the weekly Microprocessor
. For insights on recent industry news, subscribe to the
company’s free email newsletter: Linley


Candace Doyle
[email protected]

error: Content is protected !!