The MIND IR is a typed, SSA-based intermediate representation designed for tensor operations and automatic differentiation.

📐 IR canon (mindc 0.10.x, RFC 0021 steps 1–3):

• Canonical data shape: IRModule (the SSA structure described below).
• Canonical text serialisation: mic@1. RFC 0001 fixed-point form; the evidence-chain trace_hash is computed over mic@3 binary (RFC 0016 GAP-1, re-anchored 2026-05-31).
• Canonical binary serialisation: mic@3 (magic MIC3). Round-trip equivalent to mic@1; carries the evidence MAP epilogue via a 0x4D sentinel.
• Compact alternative (back-compat): mic@2 / mic@2.1 (with MAP) — slated for demotion to mind-model@2 per RFC 0021 step 5.
• Stability surface: /docs/stability + mind-spec ir-stability.md.

IR Structure

MIND IR uses Static Single Assignment (SSA) form with explicit types:

// Example IR for: y = relu(x @ w)
%0 = mind.const : tensor<2x2xf32> = [[1.0, 2.0], [3.0, 4.0]]
%1 = mind.randn : tensor<2x2xf32>
%2 = mind.matmul(%0, %1) : tensor<2x2xf32>
%3 = mind.relu(%2) : tensor<2x2xf32>
mind.return %3

Core Operations

Category	Operations
Arithmetic	add, sub, mul, div, neg, pow
Linear Algebra	matmul, transpose, dot
Activations	relu, sigmoid, tanh, softmax, gelu
Reductions	sum, mean, max, min, prod
Shape	reshape, broadcast, squeeze, unsqueeze
Convolution	conv2d, maxpool2d, avgpool2d

Type Representation

IR types encode both dtype and shape information:

tensor<f32>           // Scalar
tensor<10xf32>        // 1D, static shape
tensor<2x3xf32>       // 2D, static shape
tensor<?x?xf32>       // 2D, dynamic shape
tensor<2x?xf32>       // Mixed static/dynamic

Canonicalization

The IR undergoes canonicalization passes to normalize operations:

Constant folding for compile-time-known values
Identity elimination (x + 0 → x, x * 1 → x)
Strength reduction (x * 2 → x + x for integers)
Dead code elimination

Lowering Pipeline

The normative self-host backend emits native x86-64/ELF directly from the canonical IR. The MLIR → LLVM path shown below is the downstream-interchange route for specialty targets (see MLIR Lowering):

Source (.mind)
    ↓ Parse
AST
    ↓ Type check
Typed AST
    ↓ Lower
MIND IR (High-level)
    ↓ Canonicalize
MIND IR (Canonical)
    ↓ Autodiff (if needed)
MIND IR + Gradients
    ↓ Lower to MLIR
MLIR Dialects
    ↓ Lower to LLVM
LLVM IR
    ↓ Codegen
Machine Code

Learn More

See the full IR specification at mind-spec/ir.md.

Intermediate Representation