In simple terms, edge AI refers to deploying AI applications in devices in the field. Edge AI can be used no matter what field your organization calls home, from workers on the floor of a manufacturing plant, to soldiers on the battlefield, to physicians diagnosing patients in hospital rooms.
Edge AI applications are accomplished by users at the edge of the network, where data is located, rather than in a data center or cloud computing provider. With recent advancements in edge computing technologies, the possibilities of using edge AI to your advantage are now boundless.
But implementing AI at the edge requires an understanding of infrastructure capabilities and working with a partner who can provide ruggedized devices capable of handling harsher environments and use cases.
Benefits of Edge AI
You can enjoy a number of advantages when you deploy edge AI applications. It’s about empowering your users in the field to convert data to value in real-time.
- Real-Time Insights – Equip your users with real-time information, from business intelligence to military strategy to updated patient health data.
- Faster Decision Making – Your users can react much more quickly to real-time information and make quicker, more informed decisions.
- Increased Automation – Train your machines or devices to perform autonomous tasks and maximize efficiency.
- Enhanced Privacy – Keeping more data closer to the edge means having to send less of it to the cloud, thereby increasing opportunities for data breaches.
Ruggedized Devices for Edge AI
Processing edge AI workloads in real time while protecting equipment from environmental hazards (temperature, dust, vibration, moisture, limited power, etc.) is a big challenge. Devices that support AI at the edge (such as in-vehicle or MIL-SPEC systems) are complex to design and usually only support a specific, brand-name edge cluster.
Silicon Mechanics has designed a custom ruggedized system that supports internals similar to current-gen vehicle-borne systems for field use. The Argos system is pre-configured for edge AI and inference workloads (computer vision, object detection, etc.). It operates on limited power, in a broad temperature range, and resists dust and moisture. The Argos can meet MIL-SPEC requirements and it supports NVIDIA A100 GPU for optimal performance. Plus, it be more cost effective than AWS options, without vendor lock-in. They’re an ideal way to deliver edge AI workloads to your users, no matter how harsh the conditions they operate under.
A Powerful Edge AI Platform
Using ruggedized versions of technologies from solution providers like Cachengo is another way to take full advantage of AI at the edge. Cachengo’s modular storage and compute systems can be deployed anywhere, allowing you to deliver edge AI technology with the right combination of security, scale, economics, and performance.
But how can you take full advantage of Cachengo solutions? Silicon Mechanics puts ruggedized Cachengo devices through stringent temperature/humidity, vibration, and EMI testing to ensure they operate reliably in the most challenging environments.
A ruggedized Cachengo solution from Silicon Mechanics offers these benefits:
- Enhance security with a peer-to-peer network (Hive Connect) that sits on top of Ethernet and is virtually impossible to hack or disintermediate.
- Increase scale by adding processing power with storage so that capability and capacity expand together.
- The simplified edge architecture can reduce CAPEX by 5x and OPEX by 4x compared with traditional Intel architectures.
- Add CPUs, GPUs, and even TPUs to storage to optimize analytics performance at the edge.
Use Cases for Edge AI
Edge AI applications can provide benefits in a number of industries, provided your ruggedized devices are capable of handling any environment you work in. Rugged edge components can be used for a wide variety of use cases, including:
- Geospatial intelligence (GEOINT)
- Computer vision
- Edge Inference
- Computer Vision
- Object Detection
- Anonymous Sentry
These are just a few of the many new use cases that are emerging for edge AI. The key is having an infrastructure partner that helps you take full advantage of your edge AI deployments. Learn even more about how you can realize the advantages of edge AI with Silicon Mechanics here:
This UrIoTNews article is syndicated fromGoogle News
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