Configurable hardware for AI research and product development
With a full software–hardware stack you can actually see
LeoGreenAI builds the LEO execution core, an ONNX-oriented compiler, and the CSM control and statistics path so you get hardware-accurate visibility into execution, parallelism, and utilization—not a simulation of your training environment alone.
What we offer
Research partnerships, FPGA bitstream subscriptions, and RTL IP licensing—our product lines—backed by the same compiler-to-silicon stack and in-house IP library. Deep configurability and broad model coverage are capabilities across those offerings, not separate products; see Configurability and Model zoo for detail.
Research partnerships
We provide research-ready LEO hardware, the compiler, and engineering support on a stack built for deep configurability and end-to-end visibility—so teams can see what really runs on silicon. We collaborate with partners on directions such as scale-up, scale-out, photonic links, and energy-aware training; we are the enabling platform, not a vendor of finished solutions in every niche. The aim is to support larger system studies and better model design grounded in measurable hardware behavior.
Partnerships →
Licensed bitstream subscription
Turn supported FPGAs into ML inference experimentation or product-class platforms through a licensed bitstream release channel and matching tooling.
Especially when security or a fast-changing model line makes fixed inference silicon unattractive: you keep hardware refresh on demand as ISA and accelerator features evolve—without new ASIC spins—and target inference on par with or better than GPUs on supported workloads, backed by CSM-visible measurement in your environment.
FPGA program →
RTL IP licensing
License LeoGreenAI RTL for silicon hardening (e.g. netlist handoff for integration signoff) or architectural modification—embed the LEO core and companion IP in your SoC or ASIC roadmap.
The core and memory subsystem are architected for efficiency and throughput; the memory interface is built for extendability as systems grow. Hardened IP is aimed at best-in-class energy efficiency—we do not publish public efficiency, performance, or power figures; characterization stays under NDA with agreed methodology.
RTL IP →Parallelism, hardware use, and execution time
Wall-clock inference improves when the compiler and LEO hardware jointly expose parallelism: independent work can run together where the ISA and datapath allow; tiling, fusion, and scheduling keep arrays from sitting idle; and memory traffic can be overlapped with compute so that latency to DRAM or HBM is hidden behind useful work, easing the usual memory wall problem instead of leaving the machine starved for data.
The objective is high sustained utilization: fewer pipeline bubbles, less waiting on operands, and schedules aligned with what the configured core can execute in parallel. Structural limits—shared units, ordering rules, bandwidth—still bound speedup. LeoGreenAI ties compiler artifacts to CSM counters so teams can reconcile intent with silicon and iterate across model, mapping, and hardware.