Edge-native AI – Intelligence at the Edge of the Edge

Artificial Intelligence (AI) and Machine Learning (ML) are not static technologies, they continue to expand both in terms of capability and application. Over a relatively short span of time AI and ML solutions have evolved from decentralized cloud-based architectures to applications that are smaller in footprint with more centralized control.

From the cloud, to the edge, to the edge-of-the-edge (Endpoint AI), intelligence is now being applied in ways that provide new levels of operational performance. Any industry whose competitive position is dependent upon optimal performance of machines and processes will benefit from Edge-native AI technology.

Edge-native AI Defined

What is Edge-native AI ? Simply put, it is intelligence that is specifically designed to live, train, and operate at the extreme endpoint of a device, machine, or process. It is AI and ML that is embedded directly into the microcontroller (MCU) or microprocessor (MPU) of the targeted asset. Edge-native AI revolutionizes operational control by providing a new, asset-centric, approach to making high-impact assets smarter, more secure, and more reliable.

Differentiators

Edge-native AI has several intrinsic benefits when compared to legacy technologies.

• Intelligence that trains and runs locally within the existing enterprise infrastructure. No additional infrastructure or hardware required.
• Enhanced cyber security that can execute asset data processing and visualization within the local environment. No exposure of sensitive data to external environments.
• Deeper insights into asset performance and health. Asset-specific AI algorithms deliver real-time insights into individual asset status and performance patterns.
• Customizable analytics built directly into the asset. Real-time dashboards provide performance analytics customized to the needs of the specific stakeholder.
• Elimination of scheduled scans to deliver critical data. Edge-native AI solutions deliver real-time detection of asset performance anomalies.
• Reduced cost and minimized dependance upon cloud infrastructures. A self-contained infrastructure that reduces overall operational cost.

Where Can Edge-native AI be Applied?

This new Endpoint AI technology can be applied within any industry that utilizes machines and devices to fulfill their operational mandates. That sounds very broad, and it is. This advancement in AI and ML capability will directly impact a broad cross-section of industries. The impacts will be both broad and deep. Examples will include:

• Industrial Automation
  • Predictive maintenance, enabled by asset-specific intelligence, reduces asset downtime, increases productivity, and improves overall equipment effectiveness (OEE).
  • Predictive analytics allows for more efficient utilization of production personnel and improves the performance of industrial processes.
  • Faster identification and remediation of asset performance issues improves asset uptime, health, and longevity.
  • AI-enabled security protocols protect critical assets from malicious cyber-attack and ransomware threats.

• Automotive
  • Endpoint AI and ML algorithms are embedded directly into automotive telematic control unit (TCU) devices and hardware.
  • Edge-native AI solutions are sensor and data agnostic providing additional flexibility and scalability.
  • Multi-dimensional behavioral algorithms run recursive analysis at the endpoint producing highly accurate performance data.
  • Predictive maintenance on fleet vehicles improves vehicle availability and extends vehicle service life.

• Energy
  • Intelligence that lives and trains at the asset endpoint increases mean time between failure (MTBF) for critical field machines and devices.
  • Intimate monitoring of individual production assets provides real times alerts on issues related to output volume and quality.
  • Reduction in overall operating costs via reduction in maintenance costs, increased asset production, longer asset lifespans, and reduced cloud infrastructure cost.
  • Sophisticated endpoint cyber security provides more robust protection of assets against Zero-Day cyber-attack.

• Telecom
  • Edge-native AI embeds and trains AI and ML algorithms that produce predictive analytics that improve telecom network performance and reduce operating costs.
  • Telecom operators can transmit real-time device performance data back to device OEMs and customers.
  • Self-learning security protocols embedded at the network and device levels deliver state-of-the-art security to operators, OEMs, and customers.
  • Endpoint quality of service (QoS) metrics generated by AI-enabled analytics power the fine tuning of the customer experience.

• Semiconductor
  • Edge-native AI delivers embedded AI technology that drives deeper semiconductor penetration into the IoT technology stack.
  • Endpoint AI-empowered chips provide vital chip and asset performance data back to semiconductor and device OEMs.
  • Embedded intelligence that drives growth in chip memory and networking capacity.
  • Endpoint AI training of manufacturing assets produces increased uptime, higher productions yields, and lower costs.

New Edge-native AI solutions will be a driving force behind the next wave of Industry 4.0 advancements. Intelligence at the extreme edge will deliver new levels of visibility, control, predictability, security, and profitability. Minimizing the dependance on cloud infrastructures will also create more consolidated command and control of AI-enabled device and machine ecosystems.