MACHINE INTELLIGENCE NATIVE DESIGN
Intelligence, compiled.
Deterministic computation with evidence you can verify. MIND is statically typed, with a compiler that self-hosts on its supported native path. Verified workloads produce bit-identical computational results on x86 and ARM. Hash-anchored build evidence, tensor shape checks, and compile-time autodiff support auditable numerical systems. See the verified determinism scope.
1fn main() {2 // 2x2 input tensor (dense literal, concrete types)3 let x: Tensor[f32; 2, 2] = [[1.0, 2.0], [3.0, 4.0]];45 // Elementwise operations work; inter-fn tensor args are Phase 116 let y = x .* 2.0;78 print(y);9}
Shapes and dtypes are known at compile time, so invalid tensor math never reaches production — the type checker catches what Python can't.
The problems we solve
Today's AI stacks are fragmented: Python for research, C++/CUDA for performance, separate runtimes for cloud and edge. Models fail in production with runtime shape mismatches, training loops carry per-iteration autodiff overhead, and regulated industries can't get reproducible builds.
Runtime shape bugs
Tensor shape and dtype errors surface in production, not during development. MIND catches these at compile time with static tensor types.
Fragmented toolchains
Python for prototypes, C++ for production, glue code everywhere. MIND gives you one language from research to deployment.
Non-deterministic builds
Can't reproduce training runs or audit model provenance for compliance. MIND delivers bit-identical, reproducible output across x86_64 (AVX2) and ARM64 (NEON) CPUs for its covered workloads, with a deterministic execution mode; GPU execution is through the commercial runtime.
What MIND does
A programming language and compiler stack built specifically for AI and numerical computing — tensor-native types, static shape checks, automatic differentiation, and a native-ELF backend with MLIR as the downstream-interchange path for specialty targets — all in one toolchain.
Tensor-native and statically checked
Shapes, dtypes, and device semantics live in the type system, catching whole classes of bugs at compile time instead of at runtime.
Compile-time autodiff
Gradients for scalar and core tensor ops are computed once during compilation — no runtime tape, no per-iteration graph construction.
Deterministic execution & auditable builds
Bit-identical, reproducible output across x86_64 (AVX2) and ARM64 (NEON) CPUs for the covered integer / Q16.16 and scoped strict-float workloads, verified by SHA-256 hashing in public CI. Critical for regulated industries and model certification, where determinism isn't optional.
How it works
Language & type system
A Rust-inspired language with first-class tensors, deterministic memory management, and built-in automatic differentiation.
- Shape- and dtype-aware tensors
- Differentiable functions with compiler-generated gradients
- Device annotations for CPU today; GPU execution through the commercial runtime
Compiler & runtime
MIND IR lowers to native x86-64/ELF directly in the normative self-host path (deterministic by construction), with MLIR as the downstream-interchange backend for specialty and exotic-chip targets via a pluggable backend trait.
- Native-ELF backend: the front-end self-hosts with a byte-identical bootstrap
- MLIR interchange for specialty targets; LLVM for hardware-specific codegen in that path
- Lean runtime modules for AOT, JIT, and embedded targets
Performance That Matters
MIND optimizes both compilation and runtime — fast iteration during development AND production performance when it matters.
Fast Compilation
Frontend processes ML programs in the microsecond range (parse + typecheck + IR). Instant feedback enables tight iteration during development.
Deterministic Mode
Bit-identical computational output across x86_64 (AVX2) and ARM64 (NEON) CPUs for the covered workloads, verified by SHA-256 hashing. Separate build gates check artifact reproducibility with the source, toolchain, flags, dependencies, and target held fixed. Native binaries for different ISAs have different instruction bytes.
Determinant: a byte-exact, bit-reproducible deterministic BLAS →
Low-Overhead Autodiff
Gradients computed once during compilation, not on every training iteration. No runtime tape or graph construction overhead.
Who is MIND for?
Regulated ML & audit trails
Healthcare, finance, autonomous systems — industries where model provenance and reproducibility aren't optional. MIND's deterministic builds make the whole pipeline auditable by construction.
Platform teams scaling ML infrastructure
Standardize on one language for research and production. Eliminate glue code between notebooks, services, and accelerators.
Edge & embedded deployment
Compile to lean, deterministic binaries that fit into constrained environments where interpreters and heavy runtimes are not an option.
Open core + enterprise
Community Edition (Apache 2.0): The compiler and language are open source, self-hosting, and ready to build on.
Commercial runtime + hosted offerings from STARGA, Inc.: Deterministic execution mode, audit logs, compliance tooling, and hosted control plane with SLA-backed support.