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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