About FPGA Overlay MLCore
The FPGA Overlay Machine Learning Core, is designed to provide real-time FPGA core solutions that enhance machine learning and AI performance at the edge. The focus is on delivering cutting-edge hardware modules to drive efficiency and reliability.
Advantages
Our FPGA core is designed to optimize machine learning and AI processes and drive real-time performance. Explore how our hardware solution can revolutionize your AI systems.
Supported Model Types
Technical Datasheet
Our hardware supports various model types such as MLP, CNN, LSTM, TCN, and more.
Integration Checklist
Tailored Deployment
We specialize in seamless integration to ensure that the FPGA ML Overlay Core operates harmoniously within your AI and neural network infrastructure, forming a unified and reliable workflow.
Each deployment is tailored to the application domain—industrial, medical, energy, transport, or aerospace—and leverages the built-in safeguards of the overlay.
The integration process begins by mapping your model type and I/O specifications to the FPGA fabric. Every data frame is wrapped with CRC-verified telemetry for integrity, elastic FIFO buffering for rate adaptation, and timestamp alignment to maintain sequence consistency.
Safety logic is activated automatically—hardware watchdogs, threshold limiters, and safe-mux failover prevent out-of-range behavior or loss of control in critical systems. For sensitive environments, provisioning supports AES-CTR encryption, rollback protection, and dual firmware slots to ensure field updates can be reversed if a validation test fails.
During integration, optional specialty processes can be included:
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Quantization assist (INT8/INT16/Q-format calibration for model portability)
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Hybrid bridging for host-side inference via ONNX or PyTorch streams
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On-FPGA micro-inference cores for real-time classification or anomaly detection
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Extended telemetry fields (e.g., score_q16, class_id) for synchronized ML feedback
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Event logger buffers to track safety triggers and health status
This layered integration ensures deterministic timing, verifiable safety, and long-term maintainability. Whether deployed on a single system or scaled across fleets, the ML Overlay FPGA Core transforms conventional AI hardware into a mission-ready, continuously monitored, field-updatable platform
Industries
Mission-Ready Solutions
The ML Overlay FPGA Core delivers visible gains across multiple industries by turning edge AI from best-effort into predictable, real-time performance. In industrial automation, it stabilizes vibration and torque sensing so PdM models stop missing fault precursors and actuators trip safely within milliseconds. In aerospace and defense, telemetry arrives with fixed timing and CRC-verified integrity, which keeps onboard anomaly detectors dependable during high-G or link jitter. Automotive and transport systems see faster, repeatable decisions for ADAS, battery health, and robotics, because preprocessing, windowing, and safety interlocks run in hardware instead of competing with the OS. Energy and utilities benefit from phase-accurate sampling and zero-loss streaming, improving grid fault detection and inverter control. Medical and wearable analytics gain cleaner ECG/EMG features and stable window alignment, reducing false alerts and improving clinician-grade reliability. In maritime and offshore operations, the core filters noise, buffers bursts, and enforces watchdogs so pumps, drives, and hull-stress monitors respond consistently despite harsh conditions. For IoT and smart infrastructure, on-device feature extraction cuts bandwidth by orders of magnitude while enforcing safe states during outages. Across all of these, the result is the same: deterministic latency, zero-loss data paths, and hardware fail-safes that raise accuracy, reduce false positives/negatives, and make AI outcomes trustworthy in the field.
The Difference the FPGA ML Overlay Core Makes
Without FPGA ML Overlay Core
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Software-only inference with variable latency and unpredictable timing
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Prone to data loss and signal jitter under heavy load or multitasking
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CPU/GPU overhead increases power use, heat, and response delay
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Lacks hardware-level safety, watchdogs, or fail-safe recovery
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Inconsistent results when OS or background tasks interfere
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Scaling limited across devices, environments, and models
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Lower trust and reliability for critical or autonomous systems
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Dependent on non-deterministic software timing
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Reduced confidence in mission-critical machine learning or AI tasks
With FPGA ML Overlay Core
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Deterministic, real-time inference with microsecond-level timing stability
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Zero-loss data streaming verified by CRC and elastic buffering
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Hardware-accelerated pipeline offloads computation and reduces power use
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Built-in watchdogs, threshold limiters, and fail-safe control for safety
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Consistent results regardless of OS or background task activity
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Easily scalable bitstream architecture across devices and ML models
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Mission-ready reliability trusted for industrial, medical, and aerospace systems
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Predictable performance even in high-load or time-critical environments
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Enhanced confidence and safety in all AI and machine learning operations
Industries We Serve
Contact Us
Get in Touch
Have a question or want to learn more about our FPGA core solutions? Reach out to us today.