The #AIEye Reports on Growing Market for #ArtificialIntelligence
Chips: (OTCPINK: $GTCH) (NasdaqGS: $INTC) (NasdaqGS: $QCOM)
Point
Roberts WA, Vancouver BC – February 21, 2020
– Investorideas.com (https://www.investorideas.com), a global investor
news source covering Artificial Intelligence (AI) brings you today’s edition
of The AI Eye, looking at the
market for AI Chips and featuring GBT Technologies Inc. (OTCPINK:GTCH).
Read this article,
featuring GTCH in full at https://www.investorideas.com/news/2020/artificial-intelligence/02211AIEye-AIChips-GrowingMarket.asp
As
artificial intelligence (AI) markets grow and their accompanying applications
are reshaping an array of industries, a demand for the chips that increasingly
power these applications is growing in tandem. AI chips are integrated circuits
designed for performing AI tasks, such as machine learning algorithms, more
efficiently. This is attendant on the rise of an ever-increasing multitude of
AI applications including computer vision, speech recognition and natural
language processing, in use cases as well as R&D. But while the number of
companies that produce and design AI chips is and will continue to be
necessarily finite, the application and demand for them has no foreseeable
limit.
Projections
from a Research
and Markets
report on the global AI chip market put growth from $5.65 billion in 2018 to
$83.25 billion by 2027 with a compound annual growth rate (CAGR) of 35 percent
in the forecast period.
Danny
Rittman, CTO of GBT Technologies Inc. (OTCPINK:GTCH) sheds some light
on this market, confirming the seeming inevitability of AI chips becoming
ubiquitous and highlighting their particular application in IoT systems.
AI microchips will
soon become a commodity within our daily lives. One of the prominent examples
is the IoT arena. Today's IoT systems have the capability to make decisions
based on received information, without human intervention. These systems
typically operate via specific circuitries and algorithms and become major
factors in many domains like smart cities, autonomous vehicles, environment and
agriculture, smart homes and more. A new class of microchips is now emerging
and under urgent demand; AI specific integrated circuits. AI integrated
circuits are a new family of chips that are designed to handle neural network
and other machine learning algorithms to enable a whole world of possibilities
for wide variety of applications, one of them is IoT. Machine learning
algorithms, embedded within microchips, provide robust data processing,
security, efficient power control and more, in real time. If we take smart
system's power management for example, this capability significantly prolongs
device's operation time, energy savings, and major operating costs reduction.
Another example is vast data processing which opens a whole world of
possibilities for military and civil systems like drones, robotics and medical
apparatus. The ability of analyzing images, identifying objects and providing a
real time response created an increasing demand for IoT automation and for
supporting hardware. Especially the high demand for IoT technology started a
development race for AI microchips targeted for IoT devices. This emergence of
AI based smart hardware, particularly within IoT/Mobile, image processing,
security, database management and similar, will be a key trend driving the
growth of the AI microchips market.
For
its own part, GBT announced in November last
year the implementation of its “Avant! AI technology into its Epsilon EDA
(Electronic Design Automation) program with the goal of achieving increased
reliability for microchips.” Through this, Epsilon ensures that the microchip
runs faster with lower power consumption, and it does this by performing a
reliability analysis on the microchip and its circuitry during the design phase.
Rittman explained:
“If it finds any
suspicious areas, it immediately alerts the user as he or she designs the
circuit. I would compare it to a spell check. It’s highlighting in red, IC
reliability, potential problems, as the designer works. We call it:
Reliability-Aware Design Environment. That's the biggest advantage of Epsilon
EDA tool. It's a real-time analysis for reliability."
Commenting
on the broad application of AI in EDA, Rittman said:
“This will enable
IC design houses to work more efficiently with customer budgets, knowing a
chip's life span. Using Avant! AI for the IC reliability domain will ensure
high reliability and performance ICs which are particularly crucial for areas
like aviation, space exploration, military and medicine, where human lives
depend on integrated circuits operation."
Elsewhere,
SoftBank Group Corp. (TYO:9984) subsidiary, Arm
Ltd., a semiconductor technology firm recently announced the launch of an
AI chip for small IoT devices. Combining “the Cortex-M55,
Arm's most AI-capable Cortex-M processor, and the Ethos-U55, the industry's
first microNPU for Cortex-M,” the company aims to proliferate use cases for
developers by deploying AI “to billions more devices and people.” Some of these
use cases outlined by the company include established products like earbuds,
fingerprint unlock, health trackers, predictive maintenance, smart speakers and
video doorbells. In part echoing Rittman’s comments mentioned previously, an
excerpt from Arm’s Solutions Brief reads:
“As the IoT
intersects with artificial intelligence (AI) advancements and the rollout of
5G, more on-device intelligence means that smaller, cost-sensitive devices can
be smarter and more capable. They also benefit from greater privacy and
reliability due to less reliance on the cloud or internet. By delivering this
intelligence on microcontrollers designed securely from the ground up, Arm is
reducing silicon and development costs and speeding up time to market for
product manufacturers looking to enhance digital signal processing (DSP) and
machine learning (ML) capabilities on-device.”
Not
dissimilar to what GBT’s Avant! engenders, a comment from Kishore Manghnani,
Co-founder and CEO, Shoreline IoT, similarly emphasizes the labour and
power-saving benefits of the Arm solution:
"The
flexibility, uplift in performance and ease of development that the Arm
Cortex-M55 and Ethos-U55 processors bring will enable Shoreline IoT to deliver
solutions with a much longer battery life and more complex, faster and advanced
ML algorithms right at the endpoint."
The
expansion of AI chips can equally be seen at the highest peaks of the
technology world. Chip-manufacturing juggernaut, Intel Corporation (NasdaqGS:INTC) recently
acquired
Israel-based AI chipmaker Habana Labs for approximately $2 billion. This
acquisition was shortly followed by the company’s decision to switch its AI
chip focus from Nervana Systems, which it acquired in 2016, to Habana,
according to an article from Forbes. According to that
same article, written by analyst Karl Freund from tech analysis firm Moor
Insights and Strategy, “Customer input helped make the company’s decision to
build its AI roadmap based on Habana, bringing in future enhancements from the
Nervana NNP architecture and software.” Specifically noted are ‘the advantages
of a converged architecture for inference and training,” which Habana brings.
The
official announcement for the Habana Labs acquisition noted:
“Intel estimates
the total addressable market (TAM) for AI silicon by 2024 will be greater than
$25 billion, and within that, AI silicon in the data center is expected to be
greater than $10 billion in the same timeframe.”
Then
there’s Qualcomm, a company well known for its AI, particularly through its Snapdragon
mobile platforms
and which previously had a line of CPUs called Centriq that it has since
abandoned. In April last year, Qualcomm (NasdaqGS:QCOM)
announced the Cloud AI 100
processor family of AI chips “to meet the explosive demand for AI inference
processing in the cloud.” According to an article from independent Microsoft IT
outlet Redmond, “’Inference
processing’ refers to the ability of a trained neural network to infer things
from new data inputs in which it was not specifically trained.” Keith Kressin,
Senior Vice President, Product Management, Qualcomm Technologies, Inc.,
explained:
“Our all new
Qualcomm Cloud AI 100 accelerator will significantly raise the bar for the AI
inference processing relative to any combination of CPUs, GPUs, and/or FPGAs
used in today’s data centers. Furthermore, Qualcomm Technologies is now well
positioned to support complete cloud-to-edge AI solutions all connected with
high-speed and low-latency 5G connectivity.”
Just
as there seems to be an ever-growing list of applications that AI can generate,
the consequent need for AI chips behind-the-scenes is growing alongside it. And
with this increased demand, a panoply of approaches, as demonstrated by the
previously mentioned companies, is likely to continue in this new and dynamic
market.
GBT
Technologies’ Danny Rittman notes:
“The demand for
specialized AI chips is under constant growth and the R&D cost to develop
these is high. Hi-Tech firms, starting with the leaders, are constantly seeking
experienced and knowledgeable AI experts, yet the market's growth is somewhat
tapered by the shortage of skilled workforce in this domain. In the upcoming
decade, IC design houses will massively invest in efforts to develop AI
expertise in order to respond to the increased demand for AI solutions. GBT is
one of the companies that is focusing on AI IC technology in order to be among
the worldwide pioneers that will enable smart AI microchips for a wide variety
of applications, among them are efficient power management, huge data
processing, security, intelligent imaging and more.”
Sam
Mowers, Investorideas.com
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