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Simply put, edge artificial intelligence refers to the deployment of artificial intelligence applications in field devices. No matter what your business is, from workers on the manufacturing factory floor, to soldiers on the battlefield, to doctors diagnosing patients in hospital rooms, edge AI can be used.
Edge AI applications are completed by users at the edge of the network where the data is located, rather than by data centers or cloud computing providers. With recent advances in edge computing technology, the possibilities for leveraging edge AI to your advantage are now endless.
But implementing AI at the edge requires understanding infrastructure capabilities and working with partners who can provide ruggedized equipment that can handle more severe environments and use cases.
When deploying edge AI applications, you can enjoy many advantages, allowing users to convert data into value in real time.
● Real-time Insights – Provide users with real-time information, from business intelligence to military strategy to the latest patient health data.
● Faster Decisions – Users can react faster to real-time information and make faster, more informed decisions.
● Increase Automation – Train a machine or device to perform autonomous tasks and maximize efficiency.
● Enhanced Privacy – Bringing more data closer to the edge means less data must be sent to the cloud, increasing the chance of a data breach.
Hardened Devices for Edge AI
Processing edge AI workloads in real time while protecting the device from environmental hazards such as temperature, dust, vibration, moisture, limited power, etc. is a huge challenge. Devices that support edge AI are complex to design and often only support specific brands of edge clusters.
Silicon Mechanics, for example, has designed a custom reinforcement system that supports internals similar to current-generation in-vehicle systems for use in the field.
And the UK Argos system comes pre-configured with edge AI and inference workloads. It operates on limited power, operates over a wide temperature range, and is resistant to dust and moisture. Argos can meet many requirements and supports NVIDIA A100 GPU for optimal performance. Additionally, it is more cost-effective than AWS options and has no vendor lock-in. They are the ideal way to deliver edge AI workloads to users, no matter how harsh the conditions they operate in.
Using ruggedized versions of technology from solution providers is another way to make the most of edge AI. Modular storage and compute systems can be deployed anywhere, allowing us to deliver edge AI technology with the right combination of security, scale, economics and performance.
The solution can provide the following benefits:
● Enhanced security through a peer-to-peer network sitting on top of Ethernet, making it nearly impossible to hack or disintermediate.
● Increase scale by increasing the processing power of storage, allowing functionality and capacity to scale together.
● Simplified edge architecture reduces CAPEX by 5x and OPEX by 4x compared to traditional Intel architecture.
● Add CPUs, GPUs, and even TPUs to storage to optimize analytics performance at the edge.
Edge AI applications can provide benefits in many industries, provided that the ruggedized device can handle any environment in which you work. Hardened edge components are available for a wide variety of use cases, including:
● Geospatial Intelligence
● Computer Vision
● Edge Inference
●● Computer Vision
● Object Detection
● Anonymous Sentinel
These are just a few of the many new use cases that are emerging for edge AI. The key is to have an infrastructure partner that helps take full advantage of edge AI deployments.
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