This article, published a couple of days ago, outlines how grid edge computing is transforming modern power systems. It paints a picture of massively decentralized, real-time, intelligent power networks—a perfect environment for technologies like BrainChip’s neuromorphic AI to thrive.
The piece explicitly references
neuromorphic computing as one of the emerging technologies shaping the future of the smart grid, alongside explainable AI, generative models, and collaborative AI systems.
The system-level challenges and requirements described align almost exactly with what BrainChip’s Akida and TENNs platforms were built to solve: ultra-low latency, energy efficiency, anomaly and fault detection, always-on AI, edge inference, on-device learning and security.
The smart grid + grid edge AI market is exploding, particularly due to the rise of:
- Distributed Energy Resources
- EV charging infrastructure
- Smart meters and substations
- Energy trading systems
- Real-time fault detection / predictive maintenance
Itron (the author’s company) is already building AI into edge smart grid gateways.
The market size estimates for edge AI in energy & utilities are upwards of $3B today and expected to exceed $10B+ by 2030.
If BrainChip captured even 1% of the edge AI deployments in grid systems. - including relays, sensors, inverters, load balancers, and smart meters - it could mean tens to hundreds of millions in annual IP licensing or chip sales.
The other thing worth noting is that Itron has partnered with NVIDIA. As you can see from the last screenshot, they aim to utilize NVIDIA's Jetson Orin Nano. Given the article suggests that neurmorphic computing is an emerging technlogy that allows for more efficient computing, I ownder if they're considering combining Jetson Orin + neuromorphic.
For example, central nodes might run NVIDIA (Jetson/Orin) for heavy inference & cloud analytics. And fault sensors, relays, and smart meters could utilize Akida for monitoring waveform anomalies 24/7 without draining power.
How grid edge computing is revolutionising real-time power management
Smart Energy International Jul 08, 2025
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Stefan Zschiegner
From smart meters to predictive analytics, the grid of tomorrow will be built on real-time decision-making at the edge, writes Stefan Zschiegner of Itron.
As the saying goes, “the only constant in life is change,” and that is certainly true when we consider the technological advances being made in utility power management.
The traditional model, where data flows to central control centres and back, can no longer meet the demands of today’s complex, renewable-heavy power networks. A new framework, where computing power is being utilised at the grid edge, is driving transformation of electricity management.
A decentralised framework relies on intelligent edge devices capable of detecting anomalies in real time for prevention or to take action near real-time. For example, if lightning strikes a distribution pole, intelligent field devices can autonomously detect the fault, isolate the damaged section, reroute power and adjust voltage levels—all within seconds, often before the central system even registers the event. In this new framework, edge intelligence is essential for maintaining grid stability and integrating distributed energy resources (DERs).
New architecture for new challenges
To enable this advanced intelligence, modern grid edge devices are evolving to include a rich array of features, such as advanced microprocessor relays, smart reclosers with embedded computing for autonomous fault isolation, intelligent power quality monitors with real-time waveform analysis and edge compute gateways with artificial intelligence (AI) capabilities and local storage. These devices connect through Field Area Networks (wireless mesh) for local communication and Wide Area Networks for backhaul to control centres.
As grid edge intelligence expands, central SCADA systems remain crucial. Modern architectures employ edge-first processing for time-critical decisions, hierarchical processing with multi-tier decision-making and protocol translation gateways for seamless communication.
Data flows in multiple patterns: horizontal flows facilitate peer-to-peer device communication, vertical flows maintain traditional telemetry and control, publish-subscribe models enable status updates and event-driven architectures coordinate responses across systems.
Advanced technical requirements
Grid edge computing systems must meet strict requirements, including response times of single-or-double digit milliseconds for protection functions, sub-cycle responses for power quality correction, environmental hardening to operate in extreme conditions (-40°C to +85°C) and deterministic computing for guaranteed response times.
Modern grid intelligence typically employs a layered approach with an edge layer for immediate time-critical functions, a fog layer at substations for coordination across devices and a cloud layer for analytics, machine learning (ML) and enterprise integration.
As we’ve established, reaction times are key to maintaining grid integrity. The ultimate goal for modern edge systems is to operate within the microsecond range–responding faster than conventional systems and making critical decisions relating to:
- Fault detection and isolation through high-speed algorithms and adaptive protection.
- Power quality management with real-time harmonic mitigation and voltage compensation.
- Load balancing via automated reconfiguration and microgrid management.
- Voltage/VAR optimization through real-time control and reactive power management.
AI and ML can enhance these capabilities through pre-trained algorithms deployed on edge devices, federated learning and continuous refinement of decision-making. ML-enhanced systems using pre-trained AI platform chips can produce a performance 80 times greater than the same algorithm running on an Intel i5 processor without acceleration.
The impact of AI also transforms edge intelligence grid management from reactive to predictive. Deep learning and advanced analytics enable equipment health scoring based on operating conditions, time-to-failure predictions, optimized maintenance scheduling, AI-based anomaly detection and integration of environmental factors into predictive models.
In addition, the future of modern edge systems lies in enhancing grid stability through real-time load balancing made possible by multi-timeframe load forecasting, continuous power flow optimization, real-time phase monitoring and balancing, and customer load participation through automated control mechanisms.
The future of grid edge computing
Emerging technologies are advancing grid edge intelligence through explainable AI (xAI) for transparent decision-making, neuromorphic computing for efficient AI processing, generative models for unexpected grid conditions and collaborative AI systems for decentralised coordination.
Edge-native applications are evolving with digital twins for predictive simulation, distributed ledger technology for secure transactions, autonomous grid agents for negotiation-based operation and immersive visualisation for field personnel. Integration with renewable energy systems will be crucial through direct device-to-device communication, peer-to-peer energy communities and regulatory frameworks that rely on edge intelligence.
As intelligence moves to the grid edge, security concerns have evolved due to expanded attack surfaces. With thousands of accessible devices, constrained computing resources, heterogeneous systems from multiple vendors and long-lived equipment creating legacy security concerns, mitigation strategies include defense-in-depth security, autonomous fallback modes, physical tamper protection, graceful degradation during attacks and AI-driven threat detection.
Implementation considerations
Implementing the new framework is a significant undertaking and investment, and requires total cost of ownership analysis, value stacking for multiple benefit streams and risk-adjusted return calculation. Implementation approaches may include targeted deployment in high-value locations, phased rollout of capabilities and test bed validation.
The human element remains a critical success factor. Bridging the skills gap requires structured role-based training programs, simulation training and formal certification to ensure operational readiness and long-term workforce capability.
Regulatory compliance is equally essential. Navigating frameworks such as NERC CIP (North American Electric Reliability Corporation Critical Infrastructure Protection) requires robust cyber-security measures when entities are operating, controlling or interacting with the North American Bulk Electric System (BES) to protect against cyber threats and ensure grid reliability. In addition, organisations must meet reliability-reporting obligations, adhere to data privacy compliance and maintain detailed documentation to support regulatory audits and insight.
Finally, success is measured across three key metrics: technical performance (including response time and detection accuracy), operational benefits (such as improved reliability and reduced outages) and financial outcomes (like cost savings).
Conclusion
As we move into an era of DERs, intelligence at the grid edge has become critical for maintaining a reliable power system. The transition from centralised to distributed intelligence represents a fundamental shift. The old principle of “centralise for optimisation, distribute for reliability” is giving way to “distribute intelligence to act where the problem occurs.”
The grid of tomorrow—sustainable, resilient and responsive—will be built on real-time decision-making at the edge. The future belongs to those who can set direction centrally but act locally, at the speed modern power systems demand.
Stefan Zschiegner of Itron writes on tech advances in utility power management, which needs a new framework utilised at the grid edge.
www.smart-energy.com
www.tomshardware.com
like but downgraded it to a 
instead cause you didn't include the obligatory "Larry Gif" 
... but fair ...
Update: Akida SDK & Tools – July 2025 Release
Highlights
Full 4-bit support in Akida 2.0
Dynamic input shape support
Extended analysis & optimization
Improved ONNX workflow
What does this mean for developers?
This Is Not Just Research — It’s Strategic IP
Bottom Line: Why It Matters
