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| The Golden Triangle of Smart loT Edge Operations-"Software and Hardware Integration, Accelerated Implementation and Concept Sharing" |
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| Joe Lo, General Manager, Aetina Corporation. |
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| If we look at the current technology trend, people have been discussing IoT, artificial intelligence (AI) and other relevant terms for the better part of a decade, believing these fields to be the definition of future development. However, when such an era will become a reality is still subject to discussion. With AI and IoT seeing continuous development in the last two years, the smart industry has set data as top priority. As edge computing is used to filter terminal data, share cloud computing loading as well as and data storage, it has gradually become the main focus of the industry. As a result, how to achieve cross-industry cooperation and integration so as to maximize the benefits of introducing artificial intelligence, has become a trend that can’t be overlooked. |
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Looking back on the development of AI, it is the third time in history that AI has gone under the spotlight since the AI evolution in 1950. With the production of high-performance computers, the development of the Internet, the rise of data learning centers and big databases amid the third wave of AI development; computers are without a doubt, gradually becoming smarter. As accumulation of data becomes abundant, “Machine Learning” is inevitable. Many industry-leading companies such as Google and Microsoft also leverage AI’s deep learning to identify information that cannot be symbolized, such as images and waveforms, in attempt to implement more possibilities in the use of AI.
Examples of actual AI application include day-to-day services provided by Google and Amazon, customer service robots (Chatbot) for numerous banks and the epidemic prevention health barcodes recently invented by Ailpay. These inventions indicate the arrival of the smart era. In addition to consumer products, AI has seen earlier development and is more widely adopted in industrial control. First used in factory automation, AI has been continuously used in the industrial field. In summary, AI makes existing technologies more convenient and easy to use.
AI technology is implemented in the field of IPC in aims to improve and accelerate the data processing of industrial systems. The difference between IPC and PC is that the former has customized, service-oriented and small-volume large variety features. Moreover, IPC operations should consider the operating environment of each application. The performance and applications required by each field are different as well. Therefore, how to achieve real-time control and facilitate decision-making, thereby reducing the time it takes to determination on data sent to edge computing and creating a smoother processing solution will be key to industrial application. The core concept of Industry 4.0 is to achieve technical integration of industrial processes. As Industry 4.0 heavily relies on the machine interactivity and effective operation, edge computing (which has the ability to make good use of factory bandwidth, improve privacy safety and reduce latency) has become the direction for future AI technology development.
“Edge Computing” is obtaining data from a point closest to the data source and relieving cloud loading by processing a huge amount of data and making on-site determinations and inferences so as to effectively reduce the latency. Edge computing is no doubt the best solution for the development of AI applications and to an extent, aligns with the market positioning of Aetina Corporation. Since its establishment, Aetina has concentrated on developing GPU solutions for industrial control, focusing mainly on the development of high-performance GPGPU. In the past two years, Aetina’s development has aligned with edge computing and equipment enhancement trends. We acknowledge Nvidia’s forward-looking groundwork layout in the AI field, and therefore rely on their Jetson module in most of our edge computing operations. Factoring in the high variety of AI market demands as well as the fact that AI applications can be divided into high, medium and low level applications, discovering the most suitable product for a business is the current market trend. For this reason, Aetina also offers different products in response to the various needs of our clients.
With hundreds of businesses working in the development of AI technology, new developers entering the AI market will most likely need to overcome four different challenges: (1) Difficulties in the integration of peripheral software and hardware, (2) Difficulties in cross-industry communication, (3) Lack of experience to resulting in extended development time and (4) Management, maintenance and safety concerns of edge devices. In order to solve the aforementioned problems, Aetina proposed the “SparkBot” project in this year’s edge computing strategy proposal, in hopes of adding value to hardware equipment upgrades. Starting with software, the project optimizes operating systems, integrates different technologies and cooperates with software and algorithm companies to improve platform efficiency. Aetina’s modern approach is not subject to software or hardware limitations. Furthermore, it also provides developers with different edge smart application concepts and strengthens the management and security level of numerous devices. This way, Aetina is one step closer to building a strong and powerful industrial ecosystem.
The “SparkBot” project mainly proposes solutions to aforementioned problems through three different aspects: pre-integration, accelerated implementation and application concept sharing. The concept of pre-integration is to integrate related software and hardware into the Jetson platform in advance to maximize the platform’s flexibility. Accelerated implementation refers to the integration of the ROS system, preparing a manage platform for customers and expanding application possibilities. Furthermore, it aims to simultaneously track system maintenance and application effectiveness of platform users, as well as making edge computing more user-friendly. Finally, application concept sharing combines all application concepts through experimental robots to provide business partners with more ideas and samples. By doing so, clients will be able to think about how to apply such ideas to their business.
In terms of smart applications, Aetina launched three experimental robots based on smart logistics applications, including warehouse robots and self-driving vehicles. The featured functions include warehouse control and management, access management and indoor air quality control. The core value of the SparkBot experimental robot is to inform the developers that Aetina provides a hardware platform, as well as demonstrate software and hardware technology. Aetina chose to showcase SparkBot using robots in order to present its edge computing smart groundwork layout in a way most easily understood by the developers. This will facilitate cooperation and communication between us and clients, further accelerating the implementation of each device.
In 2020, AIoT will see continuous development and edge computing will become a mainstream trend. In alignment with this trend, Aetina looks forward to leveraging accumulated expertise and combine comprehensive technology demonstrated by its “SparkBot” project in undergoing constant learning and making roll planning adjustments; with the ultimate goal of creating future smart solutions. The development of AIoT is not an independent process, it is also a challenge for cross-industry cooperation. Aetina will continue to establish business partnerships with relevant manufacturers and look forward to jointly creating an international smart living environment.
Written by Joe Lo, GM of Aetina Corp. Translated by Fohigh Technological Translation Co., Ltd. |
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