MIND MIND | Machine Intelligence Native Design
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MIND Features Overview

Features

MIND combines a tensor-native language, differentiable programming, and a modern compiler pipeline to give you a unified environment for AI development.

Tensor Cube Schematic

Tensor-native type system

Tensors are first-class types, not just library objects. Define shapes and dtypes in signatures and let the compiler infer the rest.

  • Compile-time validation of tensor dimensions
  • Shape inference across function boundaries
  • Safer refactors: incompatible changes fail at build time
Compiler Pipeline Schematic

MLIR + LLVM compiler pipeline

MIND lowers into a dedicated MLIR dialect for tensor and graph optimizations, and then into LLVM IR for hardware-specific code generation.

  • Operator fusion and layout optimization at the MLIR level
  • Reuse of LLVM's mature optimization passes
  • Support for x86, ARM, RISC‑V, WebAssembly, and more
Autodiff Graph Schematic

Built-in automatic differentiation

Mark functions as differentiable and let the compiler generate optimized gradient code at the IR level.

  • Source-transformation AD in the compiler pipeline
  • Gradients as first-class functions
  • Optimizations applied to forward and backward passes
Device Chip Schematic

Device semantics in the language

Express where computations should run and get compile-time checks that your program matches device capabilities.

  • Device annotations for CPU, GPU, and future accelerators
  • Compile-time validation of unsupported ops on targets
  • Multi-target builds from a single source codebase
Security Shield Schematic

Deterministic builds & safety

The compiler is written in Rust and produces deterministic, reproducible binaries. Fully auditable runtime execution.

  • Rust-style memory safety and concurrency guarantees
  • Bit-for-bit reproducible builds given the same inputs
  • Lean runtime surface area for secure deployments
Open Core Schematic

Open-core, extensible design

The core compiler and language are MIT-licensed. Enterprises can extend MIND with private backends, passes, and runtimes without forking the language.

  • Open-source core for community innovation
  • Pluggable executors for custom hardware
  • FFI hooks for C/C++ and Python interoperability