Floating-point reference model to fixed-point implementation to production RTL, with dynamic range analysis behind every word-length decision and testbenches that prove the hardware matches your algorithm bit for bit.
First range analysis free, then from $4,900 →Conversion is where AI earns its place. It reads a floating-point reference end to end and proposes the types, and where the work is repetitive and measurable we run it agentically, with agents driving the simulator in a loop until the accuracy target closes. Every number that reaches you has been checked by an engineer.
Your MATLAB or C reference is read in full, the datapath mapped, and word lengths, rounding modes, and overflow behavior proposed per signal rather than applied uniformly.
Word-length optimization is a loop: instrument the model, run stimulus, measure SNR against the float reference, adjust bit widths, repeat. Agents run that loop themselves until the error budget closes.
An FPGA and DSP engineer reviews the range analysis, the chosen types, and the RTL before delivery. Nothing generated leaves without a person having read it.
The risk in this work is never the syntax. It is quantization: a fixed-point model that drifts from your algorithm in ways nobody notices until the hardware is in the field. Every stage below is verified against the one before it.
Your MATLAB or C floating-point algorithm becomes the reference model, with a test vector set that defines correct behavior.
The reference is instrumented and profiled over real stimulus to find the actual range every internal signal reaches.
A MATLAB fixed-point version with word lengths, rounding, and saturation chosen from the measured ranges, not guessed.
Synthesizable VHDL or Verilog that matches the fixed-point model exactly, written to production standards.
Testbenches and test cases that compare RTL output against the reference and report the error metrics you care about.
Your first dynamic range analysis is free, with no card and no commitment. Conversions are priced by the size of the algorithm. Larger than the listed tiers? Send it over for a free scope.
Guessing bit widths costs you either accuracy or area. Profiling the algorithm over representative stimulus tells you exactly how many integer and fractional bits each signal needs.
A conversion you cannot verify is a conversion you cannot trust. Every delivery includes the testbenches and test cases that demonstrate equivalence.
RTL output checked against the fixed-point model sample by sample, so any divergence shows up immediately rather than in silicon.
Test cases targeting saturation limits, zero crossings, maximum gain, and the input patterns that stress your specific algorithm.
Your own captured data run through both models, because random vectors rarely exercise the ranges that matter in practice.
SNR, maximum absolute error, and bit-growth summaries against the float reference, measured rather than asserted.
A working floating-point implementation and a set of representative input data is enough to start. A written spec helps but is not required.
A fixed-point error does not crash. It quietly degrades accuracy or wraps once every million samples, and it is found late and debugged slowly.
Your DSP or ML engineers stay on the algorithm while the numerics and hardware mapping are handled by people who do it constantly.
Oversized word lengths are the most common source of a design that will not fit. Measured ranges keep the datapath as narrow as it can safely be.
Conversion is the first stage. The services below carry the result forward.
Wrap the converted datapath in the surrounding architecture, interfaces, and control logic.
An independent review of the delivered RTL, including a dedicated DSP and fixed-point category.
Build the design and run your test vectors against it on real FPGA hardware over an API.
A floating-point model and some representative input data is enough for us to scope the conversion and quote it.