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Roadmap

The MIND language is evolving rapidly. Below is the current status of key components in the 1.0 toolchain.

Full-Stack AI Vision

MIND is building toward a full-stack platform for AI development — from model training to production deployment. The compiler, standard library, and toolchain are shipped open-source; GPU acceleration and distributed execution ship in the commercial mind-runtime, available under license today.

Distributed Execution

Scale models across clusters with automatic sharding and gradient synchronization.

Production Deployment

One-command deployment to cloud, edge, or on-premise with built-in serving infrastructure.

End-to-End Integration

Seamless data pipelines, model versioning, and monitoring from a unified platform.

GPU Performance (Enterprise)

The CUDA backend delivers production-grade GPU acceleration on NVIDIA hardware. It ships in the commercial mind-runtime (available under license) and is not part of the open-source v0.7.1 compiler release; the capabilities below describe the shipping backend.

Caching Allocator

Reuses device buffers to avoid per-op cudaMalloc overhead on the allocation hot path.

TF32 / FP16 MatMul

TF32 Tensor Cores with cuBLASLt. FP16/FP8 support for Ada Lovelace and newer GPUs.

Vectorized Elementwise

float4-vectorized elementwise kernels targeting near-peak device memory bandwidth.

Benchmarked on an Ada-class GPU (sm_89). Performance scales with GPU capabilities. Enterprise license required.

Performance Roadmap

With the CUDA backend in the commercial mind-runtime, MIND continues optimization at the compiler layer while multi-vendor GPU and specialty accelerator targets are in progress.

Commercial — mind-runtime

Enterprise: CUDA Backend

CUDA backend in the commercial mind-runtime, separate from the open-source compiler. Target gains in allocation and TF32 MatMul throughput. Enterprise license required.

Planned

Multi-Backend: ROCm, Metal, WebGPU & WebNN

ROCm (AMD), Metal (Apple Silicon), WebGPU, and WebNN backends are planned for the commercial mind-runtime. No public release date set.

Q3–Q4 2026: Compilation Opts

Long-term target: sub-1 µs compilation (current shipped floor: 2.80–17.10 µs), incremental compilation, result caching.

Ecosystem Evolution (2026)

Strategic roadmap for evolving MIND from a specialized safety-critical tool into a broader standard for high-assurance AI.

Shipped — Q1 2026

Regulatory & Compliance Toolkit

SLSA L3 provenance, SBOM generation (SPDX 3.0 + CycloneDX 1.5), audit logs, mind_audit CLI, and regulatory checklists for FDA, EU AI Act, ISO 26262.

Shipped — Q1 2026

Model Examples & Migration Guide

CNN, autodiff, policy, edge models, and FFT examples. PyTorch → MIND migration guide with side-by-side comparisons live on the docs.

Shipped — Apr 2026

Python Bridge Tooling

PyTorch (ONNX) and JAX (XLA HLO) transpilers in mind/tools/pytorch_bridge/. AI-assisted proof prompts for UNSAT failures. 11/11 unit tests passing.

Q3 2026

Verified Model Zoo & HF Adapters

Expand examples into an evidence-chained model zoo with formal proofs. HuggingFace adapters with safety wrappers for popular architectures.

Q3 2026

Scalable Verification

Tiered verification (L0-L3) with abstract interpretation. Incremental verification with proof caching.

Q4 2026

Interactive Determinism Playground

Write a MIND kernel in the browser and get instant cross-substrate verification — proof your code emits byte-identical results on x86 and ARM today, with WebGPU on the roadmap. Paired with code-through-the-RFC interactive lessons (evidence chains, IR unification) as a learn-by-building onboarding path.

Planned

Hardware Coverage

CPU runtime shipped. CUDA backend in commercial mind-runtime. Broad accelerator coverage (ROCm, Metal, WebGPU, WebNN, TPU, NPU, LPU, DPU, FPGA, ASIC, Cerebras, Tenstorrent, SambaNova, Graphcore IPU, Intel Gaudi) is planned. Verification-as-a-Service planned for Q4 2026.

Planned

Pixel-Native Document Stack

A pixel-native document plane built from recently open-sourced engines: long-horizon OCR (constant KV cache, hundreds of pages in one pass, fully local/offline) for ingestion, paired with screenshot-based retrieval over rendered page pixels — no lossy OCR/chunking in the retrieval path. The same bet MIND makes elsewhere: don't lossily transform the artifact before reasoning over it (the retrieval-layer analogue of keeping the IR canonical). Both halves operate on one render-once artifact, with a governed audit trail of which document was indexed when.

Full details in the Ecosystem Evolution Roadmap specification.