AI Edge Computing: Deploying Rugged Edge Computers for AIoT

Artificial Intelligence of Things (AIoT) is quickly becoming a reality. The demand for AIoT applications to utilize large data sets is putting strain and high costs on cloud computer networks, which can be relieved by putting increase processing performance in proximity with a rugged edge computer and AIoT hardware such as an embedded GPU or industrial IoT device. With the introduction and rapid adoption rate for AIoT technology, edge computing has quickly transformed from instrumentation and simple energy efficient Industrial Internet of Things IIoT Gateways to much more powerful computational devices with integrated GPUs. Pairing HPC (High Performance Computing) at the edge with lower latency technology such as 5G allows for high accuracy AI modelling with reduced overheads thanks to the systems being less dependent on cloud computing and services.

Edge AI Computers are showing its potential as a game-changer for many industrial applications as we know it, enabling new ways of working that are not only more powerful but also more efficient than ever before. Adopting Edge AI Computers into process-intensive applications involving advanced technologies like AI (Artificial Intelligence), ML (Machine Learning), Deep Learning, and IIoT (Industrial Internet of Things), stems rapid development in the market for chip-level solutions, boards (SOMs and SBCs) and embedded Box PC solutions.

AI Edge Computers and AI Edge Servers aren’t always installed in forgiving environments or climate-controlled server rooms. In most use cases and to be data centric, they are installed within proximity of the data source putting them in the exact conditions to the rest of your operations. This can put your hardware in harsh environments subject to dust or water ingress, variable temperature swings, interference from EMI, or having to endure repeat shock and vibration. In these use cases, deploying industrial and ruggedized hardware at the edge is you only option to avoid system failures.

Our rugged edge computers for AIoT are designed and configured for unique use cases. Configured with either a GPU or choice AI Accelerator on board, our edge AI systems are available in multiple form factors from board level solutions to small form factor Box PC solutions, to traditional 19″ rackmount systems. These systems can be preconfigured with your choice of OS and edge AI software before dispatch.

As businesses increasingly look to artificial intelligence (AI) to gain a competitive edge, they are turning to rugged edge AI computers that can provide the real-time processing and decision-making needed to support critical applications. Edge AI solutions offer many benefits over traditional cloud-based AI, including improved reliability, lower latency, and reduced data costs. By bringing AI processing closer to the data source, edge AI can provide faster and more accurate results, which is especially important for time-sensitive applications such as retail customer service or industrial process control. In addition, edge AI computers are often less expensive to operate than cloud computing AI, due to the lower data costs associated with transferring data to and from the cloud. As businesses continue to adopt AI technologies, edge AI is likely to play an increasingly important role in supporting critical applications.

Summary of Advantages for Edge AI Computers

Real-time Analytics with Reduced Latency and Higher Speeds: Real-time analytics is a major advantage of rugged edge computers. Edge AI brings high-performance computing capabilities to the edge, where sensors and IoT devices are located. Transfer of data back & forth from the cloud takes time. Edge AI reduces latency by processing data locally (at the device level). Processing this data locally in turn significantly improves processing speed as compared to cloud AI computing.

Reduced Bandwidth Requirement and Cost: AI processing on the edge makes a highly cost-efficient alternative to cloud solutions because only processed data required or valuable will go to cloud, reducing both bandwidth and cloud storage requirements.

Improved Data Security and Reliability: Higher levels of security combined with greater speed produce greater the reliability of Edge AI System. Local processing allows Edge AI Systems cutting-edge technology to reduces the risk of cyber security breaches. The majority of data processing occurs locally, which greatly limits exposure to hackers who may try accessing your sensitive information. TPM 2.0 hardware security standards by the Trusted Platform Group ensure that edge devices have secure data storage and encryption authentication to protect your personal information from hackers or other online threats.

Highly Scalable: The potential of scalability in rugged edge computing is quite tangible through the process offloading. Reducing overheads on cloud AI computing can save valuable resource for process intensive requirements.

Reduced Power Requirements: Edge AI helps save energy by processing data at the device level. System-on-Modules such as the NVIDIA Jetson platform can provide high levels of accelerated GPU computing in low power and fanless envelopes.