COVER FEATURE
deployed across an entire system of nodes. If an edge AI inference platform can accelerate the full application stack, with data ingestion, inference, localised control, connectivity, and more, it creates compelling possibilities for system architects.
Flexibility of CPU+GPU engines for the Edge NVIDIA developed the system-on- chip (SoC) architecture used in its Jetson system-on-module (SoM). As applications for them grew, these small, low power consumption SoCs evolved with faster Arm CPU cores, advanced NVIDIA GPU cores and more dedicated processor cores for computer vision, multimedia processing, and deep learning inference. These cores provide enough added processing power for end-to-end applications running on a compact SoM. AI inference can be implemented many ways. There are single-chip AI inference engines available, most with 8-bit fi xed point math and optimised for a particular machine-learning framework and AI model. If that framework and fi xed point math works, these may do the job. Many applications call for fl exible CPU+GPU engines like those on Jetson modules. With AI models ever changing, accuracy, a choice of frameworks and processing headroom are important. Inference might need 32-bit fl oating point instead of 8-bit fi xed point math – precision experiments on a CPU+GPU engine are easy. If research suggests an alternative inference algorithm, GPU cores can be reprogrammed easily for a new framework or model. As control algorithms get more intense, a scaleable multicore CPU handles increased workloads.
Pillar 1: Scaleable system-on- modules for Edge AI From entry-level to server-class performance, NVIDIA Jetson modules are the fi rst of three pillars for edge AI inference. Sharing the same code base, Jetson modules vary slightly in size and pinout, with features like memory, eMMC storage, video encode/decode, Ethernet, display interfaces, and more. A complete comparison of NVIDIA Jetson module features can be found at:
nvidia.com/en-us/autonomous-machines/ embedded-systems/
automationmagazine.co.uk
Pillar 2: SDK for Edge AI inference applications
The second pillar converts a large base of NVIDIA CUDA developers into AI inference developers, with a software stack running on any NVIDIA Jetson module for “develop once, deploy anywhere”. The NVIDIA JetPack SDK runs on top of L4T with an LTS Linux kernel. It includes accelerated libraries for cuDNN and TensorRT frameworks, as well as scientifi c libraries, multimedia APIs and the VPI and OpenCV computer vision libraries.
JetPack also has a NVIDIA container
runtime with Docker integration, allowing edge device deployment in cloud-native workfl ows. It has containers for TensorFlow, PyTorch, JupyterLab and other machine learning frameworks, and data science frameworks like scikit-learn, scipy and Pandas, all pre-installed in a Python environment.
Developer tools include a range of debugging and system profi ling tools including CPU and GPU tracing and optimisation. Developers can quickly move applications from existing rule- based programming into the Jetson environment, adding AI inference alongside control.
For a complete description of NVIDIA Jetson software features, visit: developer.
nvidia.com/embedded/develop/software
Pillar 3: Ecosystem add-ons for complete solutions The third pillar is an ecosystem of machine-vision cameras, sensors, software, tools and systems, ready for AI-enabled applications. Over 100 partners work within the NVIDIA Jetson environment, with qualifi ed compatibility for easy integration. For example, several third parties work on advanced sensors such as lidar and stereo cameras, helpful for robotics platforms to determine their surroundings.
Systems for mission-critical Edge AI inference Many Edge AI inference applications are deemed mission-critical, calling for small form factor computers with extended operating specifi cations. Advantech created the compact MIC-700AI Series systems, targeting two diff erent scenarios with full range of performance options. The fi rst scenario is the classic
industrial computer, with a rugged form factor, installed anywhere near equipment requiring real-time data capture and control processing. These scenarios often have little or no forced air cooling, only DC power available and DIN rail mounting for protection against vibration. For this, the MIC-700AI series brings AI
inference to the edge. Designed around the low-power NVIDIA Jetson Nano using advanced thermal engineering, the fanless MIC-710AI operates on 24VDC power in temperatures from -10° to +60°C. With an M.2 SSD, it handles a 3G, 5 to 500Hz vibration profi le. The MIC-710AI features two GigE ports, one HDMI port, two external USB ports, and serial and digital I/O. For expansion, Advantech iDoor modules are mPCIe cards with cabled I/O panels. iDoor modules handle Fieldbus, wireless, and more I/O.
Mid- to high-performance image classification
The second scenario involves machine vision and image classifi cation, where cameras look for objects or conditions. Systems often use Power over Ethernet (PoE) to simplify wiring.
At the high end with the NVIDIA Jetson AGX Xavier, the MIC-730IVA provides eight PoE channels for connecting industrial video cameras. It also provides two bays for 3.5” hard drives, enabling direct-to-disk video recording. The system runs from 0° to 50°C, using AC power. All MIC-700AI Series systems run the same software, enabling developers to move up or down and get applications to market faster.
The latest MIC-710AIL features a Jetson Nano or Jetson Xavier NX in an ultra-compact enclosure, also with iDoor module expansion These systems bring AI inference to the edge in reliable, durable platforms ready for a wide range of applications including manufacturing, material handling, robotics, smart agriculture, smart cities, smart healthcare, smart monitoring, transportation, and more.
The VISION event Visitors to the show can learn more about Advantech’s Edge AI systems, or simply access this webpage for more details:
https://www.advantech.com/products/ edge-ai-system
Automation | September 2022 9
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