Sovereign Edge AI on FPGAs
The fastest AI inference infrastructure on earth
We compile neural networks into optimized FPGA IP cores. 10x faster inference, sub-0.2ms deterministic latency, 50% less power.
By the numbers
faster inference than generic hardware
deterministic edge latency
less power consumed at the edge
Validated results
Proven on real RF workloads, not slideware
Read the HEART'25 paperAMC inference latency on FPGA
faster than an Intel Core i7-9700K on the same workload
peer-reviewed publication on AI performance for AMC on FPGA
validated on the industry-standard DeepSig RF benchmark
Latency Issues
Generic hardware cannot meet the sub-millisecond thresholds required for real-time AI systems at the edge.
Power Waste
Processors designed for general workloads consume excessive power, draining edge devices and raising infrastructure costs.
Cost Inefficiency
Poor utilization of generic silicon forces over-provisioning, creating cost inefficiency for real-time AI workloads.
How it works
From neural network to bitstream, without an RTL team
A software developer experience for AI on FPGAs & ASICs. A visual graph editor brings the ergonomics of modern dev tooling to hardware design. Think Cursor, for hardware.
Intent
Natural language prompt.
Optimize
Joint model, data and hardware optimization.
Inspect
Visual graph editor.
Compile
IP Core generation.
Deploy
Bitstream generation.
Product family
Built from silicon to software
Platform
Unified edge AI operations platform. Manage inference workloads, monitor performance, and deploy models at scale across heterogeneous hardware.
Inference Engine
Hardware-accelerated inference at the edge.
NPU SDK
Software development kit for custom silicon.
Why we win
Three defensible moats and a deterministic core
Joint Co-Design HPO
Co-optimizes model architecture, input preprocessing, and hardware configuration simultaneously no existing tool covers all three axes at once.
Software-developer UI/UX
The first visual graph editor for FPGA AI. Engineers inspect, modify topology, and see hardware implications interactively no RTL expertise required.
RF domain expertise
RF-specific quantization profiles for AMC, SIGINT, and Cognitive Radio workloads, with structural MAC-reduction strategies applied before compilation.
Deterministic by design. The compiler core generates formally verifiable RTL with no LLM in the loop a critical distinction for defense and telecom deployments where predictability isn't optional.
Industries
Built for demanding verticals
Real-time quality control at line speed
Deploy vision AI directly on the production floor. Detect defects, measure tolerances, and classify parts at the speed of the line, without cloud round-trips.
From your model to your silicon
Compile PyTorch and ONNX models straight down to VHDL, ready to run on AMD Zynq and Versal ACAP FPGA platforms.
Model formats in
Silicon targets out
Financiers
Ready to deploy at the edge?
Talk to our team about your edge AI workload.