bosdi 0.1.5

Make OSDI device models (Verilog-A compiled to .osdi) differentiable via JAX

bosdi — Batched OSDI

Experimental — bosdi is under active development. The OSDI binary evaluation path is stable and well-tested, but the Verilog-A to JAX lowering compiler (bosdi.va) is in alpha and its API may change without notice. The VA lowering depends on a custom fork of OpenVAF that exposes the compiler's intermediate representation; this fork is not yet merged upstream.

Evaluate OSDI device models (Verilog-A compiled to .osdi binaries) in batched parallel via JAX.

Two evaluation paths

bosdi provides two ways to evaluate Verilog-A compact models inside JAX:

OSDI binary path (stable)

Loads a pre-compiled .osdi binary and evaluates N device instances in parallel via Rayon inside a JAX XLA custom call. The OSDI ABI provides analytical Jacobians with respect to node voltages only (conductances dI/dV, capacitances dQ/dV). A @custom_jvp rule makes jax.grad() work through node voltages — but not through model parameters or state.

from osdi_loader import load_osdi_model
from osdi_jax import osdi_eval

model = load_osdi_model("path/to/device.osdi")
N = 1024
voltages = jnp.zeros((N, model.num_nodes), dtype=jnp.float64)
params = jnp.full((N, model.num_params), jnp.nan, dtype=jnp.float64)
old_state = jnp.zeros((N, model.num_states), dtype=jnp.float64)

cur, cond, chg, cap, new_state = osdi_eval(model.id, voltages, params, old_state)

# jax.grad works through node voltages
grad_fn = jax.grad(lambda v: osdi_eval(model.id, v, params, old_state)[0].sum())

VA to JAX lowering (alpha)

Compiles Verilog-A source directly into pure JAX/Python, producing a function that is fully differentiable through all inputs — voltages, parameters, and temperature. This enables parameter optimization, sensitivity analysis, and end-to-end gradient-based design flows that the OSDI path cannot support.

Requires openvaf-r (a custom OpenVAF fork).

python -m bosdi.va device.va

When to use which

	OSDI binary	VA to JAX
Use case	Circuit simulation (Newton solve)	Parameter fitting, sensitivity analysis, inverse design
Differentiable w.r.t.	Node voltages only	Voltages, parameters, and temperature
Performance	Fast — Rayon-parallel C/Rust, batched XLA FFI	Pure Python/JAX — slower per-eval, but composable with jax.jit/jax.vmap
Maturity	Stable	Alpha
Dependencies	None beyond bosdi	openvaf-r fork

The OSDI path treats the compiled model as a black box and extracts only what the ABI exposes: currents, charges, and their Jacobians w.r.t. node voltages. This is exactly what a Newton solver needs, but the parameter axis is opaque to JAX — you cannot backpropagate through it.

The VA to JAX path exists to remove that limitation. By lowering the Verilog-A source into native JAX operations, every computation becomes visible to JAX's autodiff, making the model fully differentiable. This is what enables gradient-based parameter extraction, design-space exploration, and end-to-end optimization of circuits where device parameters are the degrees of freedom.

Architecture

OSDI path:
  Python: osdi_eval()  →  JAX XLA custom call
    →  C++ (nanobind/XLA FFI): unpack buffers
      →  Rust (Rayon): evaluate N devices in parallel
        →  OSDI binary: currents, conductances, charges, capacitances

VA path:
  Verilog-A source  →  openvaf-r (MIR dump)
    →  bosdi.va lowering + SCCP optimization
      →  Pure JAX/Python function (fully differentiable)

Installation

Using Pixi (recommended)

git clone https://github.com/gdsfactory/bosdi && cd bosdi
pixi run build

Using pip

pip install bosdi

Build & test

pixi run build   # compile Rust static lib + C++ extension
pixi run test    # run pytest suite

# single test
pixi run pytest tests/test_osdi.py::test_resistor_dc_evaluation -v

OSDI outputs

The OSDI path returns per-device arrays shaped by model.num_nodes (terminals + internal nodes + branch-current auxiliaries):

Output	Shape	Description
cur	[N, num_nodes]	Resistive current residual at each unknown
cond	[N, num_nodes²]	G = ∂cur/∂V Jacobian (flattened row-major)
chg	[N, num_nodes]	Charge residual at each unknown
cap	[N, num_nodes²]	C = ∂chg/∂V Jacobian (flattened row-major)

Pass jnp.nan for any parameter to use its Verilog-A default. Parameters can be addressed by name via model.param_names. See tests/test_bsim4_model_card.py for a full example.

Further reading

• OSDI technical reference — parameter handling, model introspection, output layout, host-simulator integration (companion method vs MNA/DAE), and debug utilities

Limitations

• Platform: Linux and macOS; Python 3.11+; OSDI 0.4 ABI only. .osdi binaries are platform-specific — compile from .va sources via openvaf-r on each target
• OSDI differentiability: jax.grad() works through node voltages only, not model parameters — use the VA path for parameter gradients
• Stateful models (num_states > 0): evaluation is skipped and outputs are zeroed
• VA lowering (alpha): user-defined analog function calls and noise contributions are not yet supported