By Lee Hee-man, AMD AECG Korea sales lead
Designing and scaling industrial computers is becoming tougher as the variety and number of sensors grow to fulfill the exploding appetite for manufacturing data. Furthermore, industrial and medical systems adopting automation are increasingly infused with AI, ML, data analytics software and intelligent displays. This drives the need for greater levels of diversified compute. Adaptive compute platforms designed for sensor-rich applications can accelerate development, simplify integration and sustain performance with tight power control.
Embedded PC computing trends
Edge digitalization includes sensorization, AI and machine learning across edge and cloud computing, human machine interfacing, multimedia experience, networking and integration of operational technology and information technology domains — each requiring distinct compute resources.
Take an example of a medical imaging system. Probes in medical imaging systems require heavy power for complex algorithms. The resulting data becomes useful only after it is cleaned, organized and processed; AI analytics then extract insights before results are visualized and stored via the organization's network.
This illustrates how sensorization enhances efficiency and productivity in embedded applications. These wide-ranging sensors need to be interfaced and processed in a timely manner usually in the range of milliseconds to achieve maximum responsiveness. Massive sensor deployments also feed Big Data algorithms that extract intelligence about processes and generate insights that drive improvement and next-generation product development.
Most edge systems integrate or connect to a PC. Bringing x86 computing, AI, control, sensor interfacing and processing, visualization, and networking closer together reduces footprint, power consumption and cost — allowing battery-powered robots to operate longer without larger or heavier batteries.
However, integration demands intensive hardware and software engineering effort, which continues as more sensor channels are added in pursuit of greater productivity, safety and more efficient business planning.
Flexible integration
A common approach combines the mature x86 ecosystem with adaptive compute platforms capable of real-time control, sensor interfacing and networking — ideal for machine vision, robotics, medical imaging, smart city, security and retail analytics.
Conventionally, an industrial PC acts as gatekeeper, dealing with the influx of sensor data and arbitrating whether processing will be handled on the x86 core or, if available, an FPGA-based accelerator card accessed across the PCIe interface. But this data transfer adds latency that often prevents real-time response.
The proposition of integrating sensor interfaces, AI processors and network processing onto the FPGA-based adaptive compute platform holds immense promise. Consolidating these functionalities onto a single motherboard enhances computational efficiency and reduces latency, eliminating the need for data to traverse disparate components. This integrated approach offers the potential for faster response and greater accuracy, as well as lower power consumption.
Supportive ecosystem
Adaptive compute platforms that can handle real-time sensor processing, control, networking and AI inferencing can help minimize latency, power and overall solution size. The result creates an efficient and powerful platform for embedded processing.
AMD's Versal adaptive processors embody this approach, streamlining development for sensor-heavy workloads.
With the addition of x86 processor IP, leveraging specialist adaptive compute solutions, as well as large numbers of I/O suited to sensor interfacing, the next level of integration, power efficiency, and system response is within reach. Large numbers of I/Os make it possible to connect different types of sensor and route the signals directly to be processed. This can apply to many different types of sensor such as gigabit multimedia serial link cameras, 10/25GE, LiDAR, medical probes like endoscopes and ultrasound. Moreover, additional sensor channels can be configured relatively easily when needed, thereby assisting scalability.
This approach combines the advantages of scalable sensor interfacing and heterogeneous acceleration with the advantages of the large ecosystem supporting industrial processing on x86 platforms to simplify sensing, AI, and control and networking software. Engineers can thus build the optimized embedded computer that exactly meets their needs.
They can tailor the number of sensor I/Os, bring each channel individually into the most suitable acceleration engine whether this is a CPU, real-time core, DSP, or AI engine, programmable logic and fine-tune the implementation for optimum power consumption as well as performance. The flexibility to connect the signals on any input channel to the most suitable processing engine on the chip also helps engineers handle mixed sensor criticality, according to importance and dependence on real-time determinism.
The extensive ecosystem supporting x86 embedded computing provides rich resources to support development of applications such as machine vision, medical image scanning, robot control, and others.
Lee Hee-man is AMD AECG Korea's sales lead. The views expressed here are the writer’s own. — Ed.
khnews@heraldcorp.com
