noir-optimize-acir

name	noir-optimize-acir
description	Workflow for measuring and optimizing the ACIR circuit size of a constrained Noir program. Use when asked to optimize a Noir program's gate count or circuit size.
allowed-tools	Bash, Read, Grep, Glob

ACIR Optimization Loop

This workflow targets ACIR circuit size for constrained Noir programs. It does not apply to unconstrained (Brillig) functions — Brillig runs on a conventional VM where standard profiling and algorithmic improvements apply instead, and bb gates won't reflect Brillig performance.

Measuring Circuit Size

Binary projects

Compile the program and measure gate count with:

nargo compile && bb gates -b ./target/<package>.json

Library projects

Libraries cannot be compiled with nargo compile. Instead, mark the functions you want to measure with #[export] and use nargo export:

nargo export && bb gates -b ./export/<function_name>.json

Artifacts are written to the export/ directory and named after the exported function (not the package).

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If bb is not available, ask the user for their backend's equivalent command. Other backends should have a similar CLI interface.

The output contains two fields:

• circuit_size: the actual gate count after backend compilation. This determines proving time, which is generally the bottleneck.
• acir_opcodes: number of ACIR operations. This affects execution time (witness generation). A change can reduce opcodes without affecting circuit size or vice versa — both matter, but prioritize circuit_size when they conflict.

Always record a baseline of both metrics before making changes.

Optimization Loop

1. Baseline: compile and record circuit_size.
2. Apply one change at a time.
3. Recompile and measure: compare circuit_size to the baseline.
4. Revert if worse: if circuit_size increased or stayed the same, undo the change. Not every "optimization" helps — the compiler may already handle it, or the overhead of the new approach may outweigh the savings.
5. Repeat from step 2 with the next candidate change.

What to Try

Candidate optimizations roughly ordered by impact:

• Hint and verify: replace expensive in-circuit computation with an unconstrained hint and constrained verification. This is the highest-impact optimization for most programs.
• Reduce what you hint: if you're hinting intermediate values (selectors, masks, indices), see if you can hint only the final result and verify it directly.
• Hoist assertions out of branches: replace if c { assert_eq(x, a) } else { assert_eq(x, b) } with assert_eq(x, if c { a } else { b }).
• Simplify comparisons: inequality checks (<, <=) cost more than equality (==). But don't introduce extra state to avoid them — measure first.

What Not to Try

• Don't hint division or modular arithmetic: the compiler already injects unconstrained helpers for these.
• Don't hand-roll conditional selects: if/else expressions compile to the same circuit as c * (a - b) + b.
• Don't replace <= with flag tracking without measuring: adding mutable state across loop iterations can produce more gates than a simple comparison.