Bigger inputs.
Smarter models.
Smaller footprints.
Embedded software that breaks through constraints in CPU/GPU processing and memory usage, training high data-intensity models on memory limited GPUs (requiring hundreds of gigabytes) and inference on edge devices in single-digit megabytes (requiring hundreds of megabytes).
01
Edge AI IN Constrained Devices
Multiple CNNs running in parallel at the edge with minimal CPU/GPU and memory usage.
02
ML Training and Inference for Extremely High Data Intensity
Up to 4K pixel arrays run on standard object detection models; more data means better learning and higher accuracy.
Real World Applications
Utilities:
Grid-Edge Intelligence
Real-time anomaly detection and classification for predictive grid maintenance.
Industrial: Electrical Panel and Motor Current Analysis
Condition-based monitoring of on-premise electrical infrastructure and powered equipment.
Medtech:
Diagnostic Imaging
Edge AI for resource-constrained devices; enhanced ML and inference for extremely high data-intensity applications.
Upcoming Event: Itron Inspire 2025
Gordian Co-founder Shekar Mantha will present a technical briefing at Itron Inspire 2025 on Monday, October 27th at 11:45 AM in Orlando, Florida.
The talk will cover Gordian's edge Anomaly detection and classification apps for next-generation grid-edge intelligence.
Edge ML inference
with minimalCPU/GPU and memory usage and power draw.
Why Gordian?
Shared memory across multiple models
for extremely high data-intensity model training.
Workflow-friendly
drop-in embedded software layer, no model customization required.