Accelerated

Computing

Engineering

ACE builds ultra‑efficient FPGA math engines and Deterministic Edge AI cores—custom or off‑the‑shelf—delivered in days, not months.

Mission

ACE is committed to providing the fastest path from mathematical theory to hardware reality. Through our focus on Engineering Velocity, we deliver automated 'Math-to-Silicon' synthesis that ensures absolute determinism and bit-perfect predictability for the most demanding real-time applications.

Math-to-Silicon Synthesis

Rapidly transforming complex algorithms into production-ready hardware logic.

Absolute Determinism

Guaranteeing bit-perfect, predictable performance for real-time control loops.

Dual-Use Expertise

Specialized hardware acceleration for both high-stakes Defense and Industrial sectors.

Processes and deliverables

Requirements Capture: Precise and flexible acquisition and analysis of customer specifications to ensure design alignment.
Automated Synthesis: Leveraging automated workflows to generate optimized RTL that meets every constraint.
Rapid Delivery: Immediate hand-off of the IP core, comprehensive testbench, and full documentation.

IP Library

Explore our existing portfolio as a starting point. Beyond these standard cores, customization is our core strength. We use automated workflows to rapidly transform your unique requirements into production-ready RTL, ensuring the final hardware fits your system perfectly. We deliver area optimization that standard High-Level Synthesis (HLS) simply cannot match. By pushing the boundaries of RTL efficiency, we enable you to migrate complex functions—traditionally locked in software—directly into high-performance hardware logic.

Some examples:

Neural ACE - Deterministic Edge AI on FPGA

Bridge PyTorch neural networks directly into cycle-accurate, zero-jitter FPGA silicon.

Designed for mission-critical physical dynamics, robotics, and real-time control loops where standard processors introduce latency and jitter.

Neural ACE delivers fixed-cycle INT8 execution, runtime reconfigurability without bitstream reloads, and a constant hardware footprint regardless of model depth.

Explore Neural ACE or check out an example of integrated Math Engine and Neural Network