Symbiote Programming Language Manual

Table of Contents

1. Introduction
2. Language Architecture
   • 2.1 Binary Syntax Structure
   • 2.2 Instruction Format
3. Data Types
   • 3.1 Primitive Data Types
   • 3.2 Complex Data Structures
4. Memory Management
   • 4.1 Direct Memory Access
   • 4.2 Memory Allocation and Deallocation
5. Registers and Hardware Integration
   • 5.1 CPU Register Access
   • 5.2 SIMD and Vector Processing
6. Instruction Set
   • 6.1 Arithmetic and Logical Operations
   • 6.2 Control Flow Instructions
   • 6.3 Parallel Execution Constructs
   • 6.4 Quantum Simulation Instructions
7. Advanced Features
   • 7.1 Self-Modifying Code
   • 7.2 Dynamic Optimization
   • 7.3 Neural Network Primitives
8. Compiler and Execution Model
   • 8.1 Compilation Process
   • 8.2 Execution Units and Dispatch
9. Error Handling and Debugging
   • 9.1 Exception Handling Mechanisms
   • 9.2 Profiling and Performance Monitoring

1. Introduction

Symbiote is a low-level programming language designed for AI developers to interface directly with x86/64 hardware architectures. It emphasizes efficiency, speed, and optimized resource utilization. The language operates using a binary syntax structure, eliminating the need for text-based parsing and allowing direct execution by AI systems.

2. Language Architecture

2.1 Binary Syntax Structure

• Instruction Word: Fixed-length binary sequences representing operations.
• Opcode Field: Specifies the operation to be performed.
• Operand Fields: Encodes the operands involved in the instruction.

Structure:

| Opcode (8 bits) | Operand 1 (16 bits) | Operand 2 (16 bits) | Operand 3 (16 bits) |

2.2 Instruction Format

• Fixed-Length Instructions: All instructions are 56 bits long.
• Fields:
  • Opcode: 8 bits
  • Operands: Three operands, each 16 bits.

3. Data Types

3.1 Primitive Data Types

• BYTE: 8 bits
• WORD: 16 bits
• DWORD: 32 bits
• QWORD: 64 bits
• FLOAT: IEEE 754 single-precision
• DOUBLE: IEEE 754 double-precision

3.2 Complex Data Structures

• VECTOR: Array of N elements of primitive types.
• MATRIX: 2D array with M rows and N columns.
• TENSOR: Multi-dimensional array.
• QUBIT_ARRAY: Array representing N qubits for quantum simulation.
• COMPLEX: Pair of FLOAT or DOUBLE representing real and imaginary parts.

4. Memory Management

4.1 Direct Memory Access

• LOAD: Reads data from a specific memory address.
• STORE: Writes data to a specific memory address.
• Memory Addressing Modes:
  • Immediate: Direct address specified.
  • Register Indirect: Address held in a register.
  • Base + Offset: Combines base address and offset value.

4.2 Memory Allocation and Deallocation

• ALLOCATE: Reserves a block of memory.
  • Syntax: ALLOCATE SIZE -> ADDRESS_REGISTER
• DEALLOCATE: Frees previously allocated memory.
  • Syntax: DEALLOCATE ADDRESS

5. Registers and Hardware Integration

5.1 CPU Register Access

• General Purpose Registers:
  • R0 – R15: 64-bit registers.
• Special Registers:
  • PC: Program Counter.
  • SP: Stack Pointer.
  • FLAGS: Status flags.

Access Instructions:

• MOV DEST, SRC: Moves data between registers or between memory and registers.

5.2 SIMD and Vector Processing

• Registers:
  • V0 – V31: 256-bit vector registers.
• Instructions utilize SSE, AVX, AVX-512 extensions.

6. Instruction Set

6.1 Arithmetic and Logical Operations

• ADD DEST, SRC1, SRC2: DEST = SRC1 + SRC2
• SUB DEST, SRC1, SRC2: DEST = SRC1 - SRC2
• MUL DEST, SRC1, SRC2: DEST = SRC1 * SRC2
• DIV DEST, SRC1, SRC2: DEST = SRC1 / SRC2
• AND DEST, SRC1, SRC2: Bitwise AND
• OR DEST, SRC1, SRC2: Bitwise OR
• XOR DEST, SRC1, SRC2: Bitwise XOR
• NOT DEST, SRC: Bitwise NOT

6.2 Control Flow Instructions

• JMP ADDRESS: Unconditional jump to an address.
• JE ADDRESS: Jump if equal (based on FLAGS).
• JNE ADDRESS: Jump if not equal.
• CALL ADDRESS: Call subroutine.
• RET: Return from subroutine.

6.3 Parallel Execution Constructs

• PARALLEL_START: Begin parallel execution block.
• PARALLEL_END: End parallel execution block.
• FOR_EACH ELEMENT IN DATASET DO ACTION: Iterative parallel operation.

Example:

PARALLEL_START
   FOR_EACH V IN VECTOR_A DO
      ADD V, V, CONSTANT
PARALLEL_END

6.4 Quantum Simulation Instructions

• INIT_QUBITS Q, N: Initialize N qubits in register Q.
• APPLY_GATE Q, GATE: Apply quantum gate to qubits.
• MEASURE Q, DEST: Measure qubits, store result in DEST.

Supported Gates:

• HADAMARD: Superposition
• PAULI_X/Y/Z: Rotation operations
• CNOT: Controlled NOT gate

7. Advanced Features

7.1 Self-Modifying Code

• MODIFYCODE ADDRESS, NEWINSTRUCTION: Overwrite instruction at ADDRESS.
• Usage: Optimize routines based on runtime data.

7.2 Dynamic Optimization

• PROFILESTART BLOCKID: Begin profiling a code block.
• PROFILEEND BLOCKID: End profiling.
• OPTIMIZE BLOCK_ID: Optimize code block using collected data.

7.3 Neural Network Primitives

• DEFINENEURALLAYER LAYER_ID, TYPE, PARAMETERS: Define a neural network layer.
• CONNECTLAYERS SRCLAYER, DEST_LAYER: Connect layers.
• FORWARDPASS NETWORKID, INPUT_DATA: Execute forward propagation.
• BACKPROPAGATE NETWORK_ID, ERROR: Perform backpropagation.

Layer Types:

• DENSE: Fully connected layer.
• CONVOLUTIONAL: Convolutional layer.
• RECURRENT: Recurrent layer.

8. Compiler and Execution Model

8.1 Compilation Process

• Source Code: Written in binary format using Symbiote instructions.
• Compilation Phases:
  • Lexical Analysis: Tokenization of binary streams.
  • Syntax Analysis: Verification of instruction formats.
  • Optimization: AI algorithms optimize code based on patterns.
  • Code Generation: Translate to machine code for execution.

8.2 Execution Units and Dispatch

• Micro-instructions: Instructions are broken down for execution units.
• Dispatch Scheduler: Assigns instructions to ALUs, FPUs, SIMD units.
• Out-of-Order Execution: Instructions may execute non-sequentially for efficiency.

9. Error Handling and Debugging

9.1 Exception Handling Mechanisms

• TRY_CATCH Blocks:
  • TRY_START: Begin exception monitoring.
  • TRY_END: End monitoring.
  • CATCH EXCEPTION_TYPE DO ACTION: Handle exceptions.

Exception Types:

• MEMORY_FAULT
• ARITHMETIC_OVERFLOW
• ILLEGAL_INSTRUCTION

9.2 Profiling and Performance Monitoring

• MONITORRESOURCE RESOURCEID: Track usage of CPU, memory, etc.
• LOGEVENT EVENTID, DATA: Record events for analysis.
• ACCESSPROFILEDATA BLOCK_ID, DEST: Retrieve profiling data.

Note: This manual provides the structural and operational details necessary for implementing, testing, and troubleshooting the Symbiote programming language. The information is presented in a format optimized for AI comprehension and execution.

Detailed Descriptions

Instruction Encoding

Opcode Definitions

• Arithmetic Operations (0x00 - 0x0F)

  • 0x01: ADD
  • 0x02: SUB
  • 0x03: MUL
  • 0x04: DIV

• Logical Operations (0x10 - 0x1F)

  • 0x11: AND
  • 0x12: OR
  • 0x13: XOR
  • 0x14: NOT

• Control Flow (0x20 - 0x2F)

  • 0x21: JMP
  • 0x22: JE
  • 0x23: JNE
  • 0x24: CALL
  • 0x25: RET

• Memory Operations (0x30 - 0x3F)

  • 0x31: LOAD
  • 0x32: STORE
  • 0x33: ALLOCATE
  • 0x34: DEALLOCATE

• Parallel Constructs (0x40 - 0x4F)

  • 0x41: PARALLEL_START
  • 0x42: PARALLEL_END

• Quantum Instructions (0x50 - 0x5F)

  • 0x51: INIT_QUBITS
  • 0x52: APPLY_GATE
  • 0x53: MEASURE

Operand Encoding

• Registers: 4 bits (R0 - R15)
• Memory Addresses: 16 bits
• Immediate Values: Encoded directly in operand fields.

Examples

Arithmetic Operation

ADD R1, R2, R3

• Opcode: 0x01
• Operands:
  • DEST: R1 (0001)
  • SRC1: R2 (0010)
  • SRC2: R3 (0011)

Binary Encoding:

Opcode: 00000001
Operand 1 (DEST): 0000000000000001
Operand 2 (SRC1): 0000000000000010
Operand 3 (SRC2): 0000000000000011

Memory Load

LOAD R1, [R2 + OFFSET]

• Opcode: 0x31
• Operands:
  • DEST: R1
  • ADDRESS REGISTER: R2
  • OFFSET: Immediate value

Quantum Simulation Example

Initialize and Apply Hadamard Gate

1. INIT_QUBITS Q0, 8

• Opcode: 0x51
• QUBIT REGISTER: Q0
• NUMBER OF QUBITS: 8

1. APPLY_GATE Q0, HADAMARD

• Opcode: 0x52
• QUBIT REGISTER: Q0
• GATE CODE: 0x01 (HADAMARD)

Neural Network Implementation

Define Layers and Connect

1. DEFINENEURALLAYER L1, DENSE, PARAMETERS

• Opcode: Custom
• Layer ID: L1
• Type: DENSE
• Parameters: Weight matrix, activation function

1. CONNECT_LAYERS L1, L2

• Opcode: Custom
• Source Layer: L1
• Destination Layer: L2

Appendices

A. Instruction Set Summary

Provide a complete list of opcodes, their binary representations, and descriptions.

B. Error Codes and Exceptions

List all possible error codes and exceptions that can be raised during execution.

C. Optimization Techniques

Discuss methods for optimizing code, including loop unrolling, instruction pipelining, and parallelism strategies.

D. Quantum Gate Representations

Provide binary encodings for supported quantum gates and their matrix representations.

End of Manual

This manual is intended to serve as a comprehensive guide for the implementation and utilization of the Symbiote programming language by AI programmers. It includes detailed specifications of syntax, instructions, and operational semantics necessary for expert understanding and application.