Edge AI (also called AI at the edge) refers to running AI-inference and sometimes model training on devices located close to the data source (for example embedded systems, robotics, sensors, industrial controllers), rather than relying entirely on cloud or data-center processing. It reduces latency, improves privacy, and often improves reliability in disconnected or bandwidth-constrained environments.

Use cases include: robotics vision, autonomous vehicles/AMRs, real-time quality inspection in manufacturing, predictive maintenance on industrial equipment, embedded IoT devices for smart cities or retail, healthcare monitoring devices.

Edge AI hardware includes embedded GPUs, NPUs, TPUs, FPGAs, microcontrollers (for very low-power), systems-on-modules (SOMs), carrier boards and ruggedized enclosures. Selection depends on compute, power, thermal, and form-factor requirements.

• Training is the process of building or updating an AI model (often done in the cloud or on high-performance hardware).
• Inference is running the trained model to make predictions / decisions in real time. Edge deployments usually emphasize inference (and occasionally on-device fine-tuning) due to resource constraints.

1. AI engineering services refer to the set of professional offerings around designing AI systems: data acquisition, model design and training, optimization (quantization, pruning), deployment and integration (especially at the edge), monitoring, retraining, lifecycle management, and integration with hardware platforms.

1. BYOM means you provide your trained AI model (for example a vision classification or object-detection model) and the engineering service (or edge platform) integrates that model into the hardware stack, optimizing and deploying it on the edge device (carrier board / module / embedded system).

Key considerations: latency requirements (millisecond responses favour edge), connectivity/reliability (edge better if intermittent), privacy/security (edge better if data is sensitive), cost and bandwidth (edge reduces data transfer), scalability and management (cloud easier for many devices). Also hardware cost, power budget, and maintenance overhead matter.

Hardware-software co-design is the practice of designing the hardware (compute, memory, IO) in conjunction with the software (AI model, inference framework, optimisation) so that the overall system is efficient, low-power, and meets performance/latency goals. This is especially important in edge AI systems. 

Some constraints: limited compute and memory resources, power/thermal limits, latency budgets, storage/flash limits, connectivity issues, hardware heterogeneity, security requirements, model footprint size

Model quantization is the process of converting model weights (and/or activations) from high precision (e.g., float32) to lower precision (e.g., int8, int4, even binary) to reduce memory footprint, inference latency and power consumption – very relevant for Edge devices.

Model pruning is the removal of less important or redundant weights/neurons from the neural network, which reduces size and compute, and helps fit the model onto a constrained Edge module.

Knowledge distillation is a technique where a large “teacher” model is used to train a smaller “student” model that meets the constraints of the edge device, often with similar performance but much smaller footprint.

The AI stack consists of layers: hardware layer (modules, boards, enclosures, like Connecttech.com's Gauntlet), middleware/firmware (CUDA, TensorRT, ONNX Runtime, device drivers), model layer (trained AI models), and deployment/inference layer (edge runtime, scheduling, orchestration). Effective edge AI engineering works across all layers. Check connecttech.com & CTaiLABS.ai for more info.

After deployment, edge AI systems need monitoring, model versioning, OTA updates, retraining to handle model drift, scaling across many edge devices, ensuring reliability and security – i.e., a lifecycle management process.

Metrics include inference latency (ms), throughput (fps or inferences/sec), accuracy (precision/recall), power consumption (W or mW), memory usage (RAM/flash), thermal behavior (temperature), model size (MB), deployment yield and reliability.

• • Edge device: the device where data is generated and inference/processing happens locally.
  • Fog: an intermediate layer near the edge (e.g., local gateway, micro-data centre) that aggregates and processes data before sending to cloud.
• Cloud: centralized data centres providing large compute/storage and long-term analytics.

Yes — a key benefit of edge AI is that inference can run locally even without network connectivity, making the system resilient and low-latency.  Book a Demo at CTaiLABS.ai

Models that are efficient in compute, memory and power. Examples: lightweight convolutional neural networks tiny ML models for microcontrollers, object-detection models pruned/quantized, anomaly-detection models for sensor data, hybrid models combining rule-based and ML.

An embedded GPU or NPU (neural processing unit) is a specialized hardware accelerator built into an edge device, designed to run AI inference workloads efficiently in terms of speed and power. See Connecttech.com for all of your hardware needs.