“AI, ML and DL will continue to drive the development of Industry 4.0 and 5.0, bringing productivity and efficiency to the next level. With the assistance of IoT and 5G technology, automation and robotics will merge with human ingenuity and creativity, giving birth to a manufacturing environment that humans could not imagine 10 years ago. FPGAs enable sensor fusion, the ability to connect with numerous IoT devices, and the balance between high performance and flexibility required for artificial intelligence systems in manufacturing environments.
Over the past three hundred years, the industry has made great strides. Machines first appeared in the 18th century, mainly powered by water and steam, and sparked the Industrial Revolution (often referred to as Industry 1.0) at the end of the 18th century. Although the concept of the assembly line can be traced back to the production of blue and white porcelain in ancient China, it was not until the end of the 19th century that Henry Ford set up the first electric assembly line, forming the framework of Industry 2.0.
Automation and computer technology came to prominence in the late 1960s and formed the rudiments of Industry 3.0, paving the way for the automation, artificial intelligence (AI) and networking solutions that drive Industry 4.0 today. Although it seems that humans are no longer visible in this picture, Industry 5.0 will lead us to return to the basics, using the precision and efficiency of AI-driven robotic systems, organically combined with the whimsy and real-time thinking of the human brain to create A more ideal manufacturing environment.
Figure 1: Evolution of Industrial Technology
Artificial intelligence (AI) is a branch of computer science that focuses primarily on developing machines that can simulate human behavior. The range of such devices ranges from simply executing algorithms to autonomously learning from their surroundings and adjusting algorithms without human intervention. Machine learning (ML) is a subset of artificial intelligence that improves specific tasks by applying statistical models derived from datasets. As a subset of machine learning, deep learning (DL) uses multiple layers of neural networks to not only perform basic machine learning reasoning, but also learn new data to gain higher-level cognitive capabilities (see figure below). In this white paper, all machine learning and deep learning will be referred to simply as ML.
Figure 2: AI/Machine Learning/Deep Learning Spectrum
Common use cases for artificial intelligence (AI) include advanced driver assistance systems (ADAS), the backbone of autonomous vehicles; speech recognition and synthesis (e.g. Huawei’s Celia); medical diagnostics; data and cybersecurity; financial services predictive models (e.g. Electronic transactions), or e-commerce and streaming service recommendations; and of course industrial manufacturing.
With the further evolution of Industry 4.0 in the early 2010s, the importance of AI in the manufacturing environment has grown. Many applications today leverage AI to make manufacturing and business operations, processes, security, and supply chains more fluid and efficient. Using predictive algorithms, AI can monitor equipment conditions, optimize maintenance schedules, and ultimately predict mechanical failures.
Manufacturing-related material supply chain management can also take full advantage of predictive algorithms to ensure that processes can continue to operate smoothly and efficiently. AI algorithms can also help predict future business by referring to past and current business needs. These AI systems can be combined with supply chain and inventory management systems to speed time-to-profit and reduce overhead costs. Robots have been an important part of Industry 3.0. And as we approach Industry 5.0, these robotic systems must have adaptive AI algorithms (mostly DL algorithms). Not only do they need to learn autonomously, they also need to be able to interpret real-time human input. Real-time adaptability with low latency will also become an indispensable element.
Ecosystem components Beyond AI
AI continues to be an important component in the thriving Industry 4.0 and the evolving Industry 5.0. However, the vigorous development of AI algorithms is inseparable from real-time data. The Internet of Things (IoT) is a system of interconnected electronic devices that can acquire and receive data from the analog and digital worlds. Time, pressure, temperature, speed, angle, and audiovisual data sources must be collected and then converted into structured data that can be analyzed and controlled by various AI-based systems. Compared to 4G networks, 5G networks deployed since 2019 (first deployed in South Korea) offer 100 times the bandwidth (up to 10 Gbps) and 500 times the number of channels. After the combination of 5G networks and IoT, the massive input data has led to a new paradigm in the computer field, namely the need for data accelerators.
In the face of massive data, the burden of data center processing and discovering the meaning behind the data has overwhelmed the traditional computing server model. In the past, the way to deal with data explosions was to add servers to the data center. The increase in the size of server installations not only increases capital expenditures, but also requires more energy to operate and cool equipment, and operating expenses also increase.
Depending on the type and load of the data accelerator, the computing power of a single data accelerator in a server can match that of 15 servers, thus significantly reducing capital and operating expenses. Hardware-based data accelerators also bring additional benefits, such as lower latency and higher stability, in autonomous vehicles, Industry 4.0/5.0, financial services, and other latency-critical use cases The effect is particularly prominent. The last characteristic of a good data accelerator is that it has excellent flexibility to adapt to changes in ML/DL algorithms, including adjustments to the algorithm itself, changes in load, and/or updates to ML/DL algorithm datasets.
There are three different hardware approaches in the data acceleration arena, namely GPU, FPGA and custom ASIC. As shown below. CPU flexibility is always the best, but there are certain disadvantages in terms of energy consumption, performance and cost compared to other dedicated data accelerators. Other options are GPUs, ASICs and FPGAs. ASICs are the most efficient and performant, but their functions are completely fixed and lack the necessary flexibility to adapt to changes in AI algorithms, parameter changes in emerging technologies, vendor requirements, and load optimization. GPU is the main force of traditional core data centers, which is limited to pure computing use scenarios, and cannot provide the networking and storage acceleration capabilities that need to be utilized in most scenarios, and has high energy consumption and cost. FPGAs can accelerate networking, compute, and storage at speeds similar to ASICs, with the flexibility necessary to provide the ideal data acceleration for today’s core and edge data centers. In addition to data acceleration, FPGAs will play a key role in areas such as sensor fusion and merging of incoming data streams, laying a solid foundation for data consumption.
Figure 3: Comparison of CPU, GPU, FPGA and ASIC
Featured Products from Achronix
Achronix develops FPGA-based data acceleration products for AI/ML computing, networking and storage applications. Unlike other high-performance FPGA companies, Achronix offers both standalone FPGA Chips and embedded FPGA semiconductor intellectual property (IP) solutions. In addition to standalone FPGA chips and eFPGA IP, Achronix also offers PCIe-based accelerator cards that can be used in development, field testing, or production application scenarios.
Built on TSMC’s 7nm process, the Speedster® 7t series FPGAs have the industry’s fastest input/output speeds, supporting 400 GbE, PCIe Gen5, and dual memory interfaces: the incredible speed of standard DDR4 and GDDR6 memory interfaces, compared to DDR4 has improved by 600%. High-speed interfaces won’t do much if data can’t easily pass through the FPGA logic array.
To avoid this bottleneck, Achronix added a two-dimensional network-on-chip (2D NoC) from the architecture, which can effectively act as a high-speed channel for all external input/output data, enhancing the functional unit blocks inside the FPGA and the FPGA logic array itself. This 2D NoC achieves more than 20 Tbps of bidirectional bandwidth, far exceeding the total bandwidth requirements of I/O and functional blocks, eliminating latency issues for on-chip communications.
In high-volume application scenarios that are sensitive to cost, performance, and energy consumption, users usually use ASICs, but how can they meet the need for flexibility? Whether it is the evolution of algorithms, changes in demand, specific requirements of suppliers and operators, protocol adaptation, or the diverse interfaces of functional system unit blocks, they all require a certain degree of flexibility.
The ultimate answer to this question is Speedcore™ eFPGA IP, which enables ASICs to have “just right” flexibility. The amount and combination of look-up tables (LUTs), memory, DSP/MLP and 2D NoC can be determined by ASIC developers, and Achronix will provide custom IP integrated on the chip for their ASIC or SoC designs.
The VectorPath™ accelerator card is a hardware acceleration platform in the PCIe form factor that can be considered as an evaluation, development and field test tool, or for production applications. The solution can also be tailored to the specific requirements of the user.
AI, ML and DL will continue to drive the development of Industry 4.0 and 5.0, bringing productivity and efficiency to the next level. With the assistance of IoT and 5G technology, automation and robotics will merge with human ingenuity and creativity, giving birth to a manufacturing environment that humans could not imagine 10 years ago. FPGAs enable sensor fusion, the ability to connect with numerous IoT devices, and the balance between high performance and flexibility required for artificial intelligence systems in manufacturing environments.