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Technical Architecture

Medi is built on a modern compiler infrastructure designed for healthcare-specific optimizations and features.

High-Level Architecture

Medi Architecture

The Medi language architecture consists of several key components:

  1. Frontend: Parser, lexer, and abstract syntax tree (AST) generation
  2. Middle-end: Type system, semantic analysis, and healthcare-specific optimizations
  3. Backend: LLVM IR generation, optimization passes, and target code generation
  4. Runtime: Standard library, memory management, and execution environment
  5. IDE & Tools: Development environment, debugger, and profiler

Compiler Infrastructure

Medi's compiler, named medic (inspired by how Rust uses rustc), leverages LLVM for code generation and optimization:

  • Lexer & Parser: Hybrid implementation combining the efficiency of Logos for tokenization with custom logic for healthcare-specific syntax and semantics
  • Type System: Statically typed with type inference and healthcare data types
  • Optimizations: Domain-specific optimizations for healthcare analytics
  • Code Generation:
  • x86-64, ARM, and RISC-V native code
  • WebAssembly for edge devices and browser deployment
  • CUDA/OpenCL for GPU acceleration

Runtime System

The Medi runtime provides key services for healthcare applications:

  • Memory Management: Hybrid approach with:
  • Low-pause garbage collection (like Go) for most operations
  • Rust-inspired manual control (scope) for low-latency IoT tasks
  • Concurrency: Built-in support for:
  • Multi-threading (OpenMP-style)
  • Asynchronous operations (async/await)
  • Distributed computing (MPI/Spark integration)
  • Healthcare I/O: Native parsers and generators for:
  • FHIR, HL7, DICOM
  • Genomic formats (FASTQ, VCF, BAM)
  • Medical imaging (NIfTI, DICOM)
  • Wearable data streams

Standard Library

The standard library is organized into domain-specific modules:

  • medi.data: FHIR, HL7, DICOM, VCF parsers and generators
  • medi.privacy: Federated learning, differential privacy, encryption
  • medi.iot: Real-time streaming and edge processing
  • medi.stats: Statistical functions for trials, epidemiology, and biostatistics
  • medi.viz: Interactive visualization and dashboarding
  • medi.compliance: Regulatory frameworks and reporting
  • medi.ai: Pre-trained models for diagnostics, predictions, and NLP
  • medi.ops: Hospital operations optimization

RISC-V Integration

Medi has special optimizations for RISC-V architecture:

  • Target Profiles:
  • RV32IMAFDC for edge devices (wearables, portable diagnostics)
  • RV64GCV for servers (with vector extensions)
  • Custom Extensions: Support for healthcare-specific instructions:
  • Genomic alignment and processing
  • Encryption for privacy preservation
  • Signal processing for medical imaging/time series
  • Optimizations:
  • Low-power operation for edge devices
  • Vector processing for parallel analytics
  • Custom intrinsics for healthcare operations

Security and Privacy

Security is a foundational concern in Medi's architecture:

  • Memory Safety: Built-in protection against common vulnerabilities
  • Encryption: Hardware-accelerated (where available) encryption for PHI
  • Access Control: Fine-grained permissions system for data access
  • Audit Trails: Automatic logging of sensitive operations
  • Differential Privacy: Built-in mechanisms for privacy-preserving analytics

IDE Integration

The Medi Studio IDE provides:

  • Visual Programming: Drag-and-drop interface for non-programmers
  • Natural Language Interface: Query and analysis using plain English
  • Code Completion: Healthcare-aware suggestions
  • Compliance Checking: Real-time validation against regulatory standards
  • Performance Profiling: Optimization recommendations for healthcare tasks

Deployment Options

Medi supports multiple deployment scenarios:

  • Traditional Compilation: Native binaries for maximum performance
  • Just-in-Time (JIT): Dynamic compilation for interactive development
  • WebAssembly: Browser and edge deployment
  • Container-based: Docker/Kubernetes packaging for cloud deployment

Future Extensibility

The architecture is designed for extensibility in emerging healthcare domains:

  • Quantum Computing: Interface with quantum libraries (Qiskit, Cirq)
  • Neuromorphic Computing: Support for neuromorphic hardware for AI tasks
  • Specialized Accelerators: Integration with healthcare-specific hardware accelerators