Future Extensions

This page outlines planned extensions to the MIND language and runtime. These features are under active development or consideration for future releases.

Phase 13: BCI & Neuroscience

Optimizations for brain-computer interface and real-time neural processing:

  • Ultra-low latency paths: Target <1ms inference for real-time neural decoding
  • Streaming tensors: Continuous data ingestion with sliding windows
  • Pre-allocated memory pools: Eliminate allocation jitter
  • Signal processing primitives: FFT, bandpass filtering, online normalization
  • @realtime annotation: Latency-critical function marking

Distributed Training

Multi-node training support for large models (see Distributed Execution Guide):

  • Data parallelism with automatic gradient synchronization
  • Model parallelism for models exceeding single-device memory
  • Pipeline parallelism for improved throughput
  • Integration with collective communication libraries (NCCL, Gloo)
  • Elastic training with fault tolerance and automatic recovery

Production Deployment

Full-stack deployment infrastructure (see Deployment Guide):

  • One-command deployment to cloud, edge, and on-premise
  • Containerized serving with auto-scaling
  • A/B testing and canary deployments
  • Model versioning and rollback
  • Built-in monitoring with OpenTelemetry integration

Sparse Tensors

First-class support for sparse data:

  • Sparse tensor types (CSR, CSC, COO formats)
  • Sparse-aware autodiff
  • Optimized sparse-dense operations
  • Graph neural network primitives

Quantization

Built-in quantization for efficient inference:

  • INT8/INT4 quantization with calibration
  • Mixed-precision training (FP16/BF16)
  • Quantization-aware training
  • Post-training quantization tools

Hardware Targets

TargetStatusNotes
x86-64 CPUStableAVX2/AVX-512 vectorization
ARM64 CPUStableNEON vectorization
NVIDIA GPUMock ReadyMockGpuBackend (CPU delegation); native CUDA 12 planned
AMD GPUMock ReadyMockGpuBackend (CPU delegation); native ROCm planned
WebGPUPlannedBrowser-based inference
Apple SiliconMock ReadyMockGpuBackend (CPU delegation); native Metal planned

Developer Tooling

  • Language Server Protocol (LSP): IDE integration with autocomplete, diagnostics
  • Formatter: Opinionated code formatter (mindfmt)
  • Debugger: Step-through debugging with tensor inspection
  • Profiler UI: Visual flame graphs and memory analysis

Learn More

See the full future extensions specification at mind-spec/future-extensions.md and the Roadmap for timeline information.