Technical foundation

Constrained Intelligence

Tilius designs perception systems around the realities of embedded deployment: limited power envelopes, bounded memory, heterogeneous accelerators, tight latency budgets, and noisy multimodal sensor data.

Embedded AI acceleration

Models shaped by hardware

Tilius designs and optimises perception models for embedded CPUs, GPUs, NPUs, FPGAs, and specialised AI accelerators. The objective is not only model accuracy, but stable performance under target runtime, memory, power, and thermal constraints.

01 Inputs

02 Preprocessing

03 Fusion

04 Model

05 Edge output

Multimodal perception

Scene intelligence

Different sensors fail in different ways. Tilius combines complementary signals to improve perception robustness across lighting, motion, range, weather, vibration, and occlusion conditions.

RGB cameras
Depth sensors
Thermal cameras
Radar
Lidar
IMUs
Event cameras
Client-specific sensors

Efficient inference

Beyond the trained network

Efficient edge inference requires coordinated model, runtime, and memory decisions. Tilius applies compression, quantisation, pruning, hardware-aware scheduling, memory-aware model design, and accelerator-specific deployment optimisation.

Real-time computer vision

Live perception

Object detection

Identify operationally relevant entities in live sensor streams.

Tracking

Maintain temporal state for objects, agents, and regions of interest.

Segmentation

Pixel-level and region-level understanding for structured decisions.

Depth

Range and spatial structure estimation for robotic and inspection systems.

Motion

Interpret movement, flow, vibration, and dynamic scene behaviour.

Scene state

Convert raw signals into actionable context for edge control loops.

Anomalies

Detect unusual events, defects, or unsafe states at the point of sensing.

Inspection

Real-time inspection pipelines for manufacturing and field assets.

Edge deployment stack

From sensors to hardware

Ingestion

Synchronised acquisition and preprocessing for heterogeneous inputs.

Optimisation

Compression, quantisation, pruning, and memory-aware redesign.

Acceleration

Target-specific scheduling across embedded CPUs, GPUs, NPUs, and FPGAs.

Inference

Low-latency execution within target power and thermal envelopes.

Monitoring

Operational visibility, update pathways, and performance validation.

Integration

Interfaces with client hardware, sensors, firmware, and application layers.

Typical deployment targets

Representative ranges for edge perception systems. Actual figures depend on model size, sensor count, hardware, runtime, and thermal envelope.

16-33 ms latency 7-25 W module power INT8 / FP16 inference 2-6 sensor streams < 8 GB memory target 30 FPS real-time output