Testbench Interop (UVM, cocotb, SVA)

A common first question: "Can I point my existing UVM / cocotb / SVA testbench at Jacquard?"

Short answer: not today. Jacquard is a gate-level engine, not a drop-in replacement for a SystemVerilog or Python simulator's testbench runtime. This page explains what works now, what is on the roadmap, and the fallback that works for any flow today.

What Jacquard drives today

MechanismWhat it isReactive?
jacquard sim + input VCDReplay a recorded input waveform through the netlistNo — inputs are fixed
jacquard cosim + peripheral modelsUART, SPI flash, JTAG, Wishbone / APB3 monitors run as GPU kernels beside the designYes — inputs can depend on outputs cycle-by-cycle

So reactive stimulus is supported — but through Jacquard's own peripheral model architecture and cosim execution model, not through an external testbench framework.

The fallback that works for any flow: record-and-replay

You do not need framework integration to use Jacquard with a UVM, cocotb, or plain-Verilog testbench. The universal path:

  1. Run your existing testbench against any simulator that can dump a VCD (Verilator, Icarus, a commercial simulator, …).
  2. Dump the design's top-level input pins to a VCD.
  3. Replay that VCD through Jacquard with jacquard sim.

This is exactly what jacquard sim already is — a recorded-waveform replay — so it works regardless of how the stimulus was generated. The trade-off is that the replay is open-loop: it reproduces the recorded inputs, so it can't react to divergent design behaviour. For closed-loop reactive stimulus, model the peripheral as a cosim kernel instead.

Roadmap

These are directions under consideration, not commitments or dated milestones:

  • SVA (SystemVerilog Assertions) — planned. Jacquard already lowers a class of immediate assertions through synthesis (GEM_ASSERT cells; see the assertion handling in aigpdk.rs). Broader SVA support is the next step here.
  • Running UVM test suites — being explored. The open question is which parts of the UVM runtime can be driven against a gate-level GPU engine versus what has to be reworked.
  • cocotb — needs more work. A naive bridge would marshal Python ↔ GPU every cycle, which would dominate runtime and erase the GPU speedup. Making cocotb performant against Jacquard needs more design thinking, not just a shim.

If any of these is blocking for you, the record-and-replay path above is the recommended interim approach.

See also