From custom ASIC and FPGA design to embedded firmware and hardware-software co-design, we build the silicon and systems foundation that brings AI inference to the edge — fast, power-efficient, and production-ready.
100+
Chip & Board Designs
10×
Edge Inference Speedup
40%
Power Reduction
<5ms
On-device Latency
RTL design, functional verification, synthesis, and physical design for custom AI accelerator chips — from concept through tape-out.
Prototyping and production deployment of AI inference pipelines on FPGA fabric for low-latency, reconfigurable acceleration.
Low-level firmware and RTOS/bare-metal integration for edge AI devices, sensors, and industrial controllers.
NPU and accelerator IP integration, driver development, and system-on-chip bring-up for AI-capable silicon.
Model quantization, pruning, and compiler tuning matched to the exact memory and power budget of your embedded target.
Schematic capture, board layout, and hardware bring-up for AI-enabled devices, from proof-of-concept to pilot run.
Real-time signal chains and sensor fusion pipelines that feed clean, synchronized data into downstream AI models.
Functional verification, environmental testing, and compliance validation before a design ships to production.
We work across the full stack — from gate-level design to the firmware and runtime that ships on it.
Both. We take projects from RTL design and verification through synthesis and tape-out for custom ASICs, and we also architect solutions around existing SoCs, FPGAs, and NPUs when a custom chip isn't the right economic call. We help you decide which path fits your volume, power, and latency targets before committing to silicon.
Yes. We quantize, prune, and re-architect trained models to fit the memory, power, and compute envelope of your target hardware — whether that's an FPGA fabric, an edge NPU, or a custom accelerator — while tracking accuracy loss against your production requirements at every step.
We design the silicon and the software stack together, not in sequence. Firmware, driver, and inference-runtime requirements shape early architecture decisions, and hardware constraints shape the model and software design — so the two never end up fighting each other after tape-out or board bring-up.
We'll review your product requirements and recommend the right hardware, firmware, and AI acceleration approach — at no cost.
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