Bringing AI to the Edge
Specto Silicon integrates AI and machine learning capabilities directly into embedded hardware products. Rather than relying on cloud round-trips for every inference, we build systems that process data on-device — where latency, bandwidth, and connectivity constraints demand it. The result is hardware that classifies, predicts, and adapts in real time without dependence on external infrastructure.
Our work spans the full pipeline from sensor input to inference output. That includes sensor fusion across accelerometers, microphones, cameras, and environmental sensors; on-device anomaly detection that flags deviations from learned baselines; and predictive maintenance models that estimate remaining useful life from vibration, temperature, and current draw patterns. Every deployment is production-ready — tested against real-world data distributions, power budgets, and memory limits — not a research prototype running on a development board.
Hardware Selection for AI Workloads
Choosing the right processor for an AI workload is a tradeoff between inference speed, power consumption, unit cost, and model complexity. We evaluate and recommend across the full spectrum: ARM Cortex-M microcontrollers running TensorFlow Lite Micro for lightweight keyword spotting and sensor classification; Cortex-A application processors for more complex vision and audio pipelines; dedicated NPUs (neural processing units) that accelerate matrix operations at a fraction of the power draw of a general-purpose CPU; FPGAs for custom datapath architectures where standard accelerators fall short; and edge GPU modules for workloads that require real-time object detection or video analytics.
Power budget analysis is central to every hardware recommendation. Always-on ML workloads — continuous vibration monitoring, acoustic event detection, environmental sensing — must operate within strict energy envelopes, especially in battery-powered or energy-harvesting designs. We model inference duty cycles, quantify per-inference energy costs, and select silicon that meets your target battery life or thermal ceiling. Thermal management is addressed alongside processor selection: sustained inference loads generate heat that must be dissipated within the product enclosure without throttling or degrading model accuracy.
Model Optimization & Deployment
Cloud-trained models rarely run directly on embedded targets. We apply quantization (converting 32-bit floating point weights to 8-bit or 4-bit integers), pruning (removing redundant parameters without meaningful accuracy loss), and knowledge distillation (training smaller student models from larger teacher models) to compress networks to a fraction of their original size. The goal is a model that fits in on-chip SRAM, meets your latency target, and maintains the accuracy thresholds your application requires.
Deployment toolchains are selected based on the target hardware. We work with TensorFlow Lite Micro for Cortex-M targets, ONNX Runtime for cross-platform compatibility across CPUs and accelerators, and Edge Impulse for rapid prototyping and data collection workflows. For custom silicon or FPGA targets, we build inference engines tailored to the specific datapath architecture. Model conversion pipelines are automated and version-controlled, so retraining and redeployment follow a repeatable, auditable process.
Memory and latency optimization extends beyond the model itself. Inference scheduling, input preprocessing (normalization, windowing, feature extraction), and output postprocessing are all profiled and optimized as part of the firmware integration. We benchmark end-to-end pipeline latency — from raw sensor sample to actionable output — and iterate until the system meets real-time requirements under worst-case conditions.
AI Application Areas
The AI systems we build serve a range of embedded use cases. Predictive maintenance for industrial equipment uses vibration, temperature, and current signatures to forecast bearing wear, motor degradation, and seal failures before they cause unplanned downtime. Voice and audio classification at the edge enables keyword detection, machine sound recognition, and acoustic event monitoring without streaming audio to the cloud. Embedded computer vision handles defect detection on production lines, object recognition for autonomous systems, and visual inspection tasks where a camera module and edge processor replace manual inspection.
Sensor anomaly detection for IoT devices identifies drift, faults, and unusual operating conditions across temperature, pressure, humidity, and motion sensor arrays — flagging events that fall outside learned normal behavior. Natural language interfaces for device control allow users to interact with products through voice commands processed entirely on-device, preserving privacy and eliminating cloud latency.
Key Deliverables
- Hardware platform recommendation report
- Trained and optimized ML model for target device
- Inference firmware integrated with sensor pipeline
- Performance benchmarks (latency, accuracy, power)
- OTA model update architecture
- Integration test suite
- Production deployment documentation
Related Services
AI integration connects directly to other stages of embedded product development. Embedded Software provides the RTOS and application-layer code that hosts inference tasks alongside device control logic. Firmware Development covers low-level driver support for sensors, accelerators, and communication interfaces that feed and surround the ML pipeline. And Hardware Testing validates that inference performance, power consumption, and thermal behavior meet specification on production hardware.