Use cases: Radar, RF classification, real-time signal processing, latency-critical ISR tasks.

FPGAs are still unmatched when you need AI to run immediately. No OS, no memory allocator, no waiting. You can’t have frame drops or model hesitation when you’re trying to sort signals mid-sweep or steer antennas in real time.

We compile models into hardware, literally. That means turning neural networks into HDL that lives alongside DSP blocks or signal chains. No runtime interpretation, no soft layers. We quantize aggressively, often to 8-bit or lower, strip anything dynamic, and route the inference path like you’d route packets, hard and predictable.

One thing that doesn’t get enough attention is partial reconfiguration. It lets us swap out model logic while the rest of the system stays up. This is critical when mission conditions change mid-flight. You don’t get that with a Jetson.

The tradeoff: Every model change is a full recompile. But for deterministic, low-latency applications, there’s still no better tool.

Use cases: Forward-deployed systems, long-life platforms, tamper-prone environments.

ASICs aren’t just about performance. They’re about trust. If the system’s going to sit in the field for years, without a patch pipeline or secure update path, you want inference that can’t be tampered with and doesn’t drift.

We build models that are co-designed with the silicon. That’s not marketing speak. We’re tuning layer structure, memory access patterns, and encryption paths during tape-out. We’ve deployed ASICs with mission locks, so the model literally won’t run if the device is out of its authorized domain or ROE. This is about enforcing policy in hardware, not trusting a runtime check to do the right thing.

The key constraint is flexibility. ASICs are frozen. But for systems where physical access is a threat vector or mission lifespan is measured in years, that’s a feature, not a bug.

Use cases: Vehicle fusion nodes, forward command posts, expeditionary AI toolkits.

This is where size, weight, and power start to loosen up a bit and you can afford to run full models. But don’t think that means it’s easy. These nodes still live in rough environments like heat spikes, flaky IO, packet loss, and uneven power draw. Your AI stack can’t be brittle.

We deploy containerized inference pipelines on ruggedized gear, with layers of fault tolerance that assume things will go wrong. That means watchdogs on inference threads, model checkpoints, and input filters that detect when sensor data starts degrading and adjust behavior accordingly.

What gets overlooked here is degradation mode behavior, not just performance under ideal conditions. Can the model downshift? Can it fail safely? Can it explain what went wrong? We build for that from the start.

Use cases: sUAS, ground robots, handheld systems, anywhere power and cooling are limited.

Embedded GPUs are appealing because they let you run real models in small packages. But most of them aren’t built to survive field conditions. They thermal throttle. They jitter. They die quietly when you least want them to.

We take stock models and retrain them for edge volatility. That means pruning, input normalization that handles bad data gracefully, and memory budgets that don’t assume you have a full Linux distro backing you up. We don’t just monitor the health of the AI. We route telemetry back into the inference loop so the model knows how healthy it is and can act accordingly.

We also design around field-recoverable failure modes. If the GPU hits thermal shutdown, inference pauses cleanly, resumes without a reboot, and doesn’t trash the mission queue. These systems aren’t just edge-deployable. They’re built to stay useful even when degraded.