Research-Stack/5-Applications/scripts/asic_nanokernel_stream_adapter.py

#!/usr/bin/env python3
"""
asic_nanokernel_stream_adapter.py - Nanokernel for ASIC Stream Adapter

This module designs a nanokernel that exposes ASIC SHA-256 hashing engines
as a general-purpose stream adapter. Instead of trying to repurpose ASIC cores
for other computations (which is impossible due to burn-in hardware), we accept
that ASICs can only do SHA-256 and expose this capability as a stream processor.

ARCHITECTURAL PRINCIPLE:
The ASIC is a SHA-256 stream processor. The nanokernel makes it accessible as a
general-purpose stream adapter. This is similar to the NES unified stack: we don't
try to make hardware do something fundamentally different - we repurpose its
existing capabilities in a new way.

NANOKERNEL RESPONSIBILITIES:
1. UART communication with ASIC chips
2. Stream buffering and chunking
3. Hash result aggregation
4. Error handling and retry logic
5. Clock rate control (via PLL)
6. Power management

STREAM ADAPTER INTERFACE:
- Input: Arbitrary data stream (bytes)
- Output: SHA-256 hash stream (32-byte hashes)
- Throughput: Configurable via PLL clock
- Latency: Deterministic based on chunk size

USE CASES:
- Password cracking (SHA-256 password hashes)
- Brute force attacks (hash-based verification)
- Data integrity verification (real-time hashing)
- Merkle tree construction (batch hashing)
- Proof-of-work mining (original purpose, but as stream adapter)
"""

from dataclasses import dataclass
from typing import Callable, Optional, List, Tuple
from enum import IntEnum
import hashlib
import time


class StreamAdapterMode(IntEnum):
    """Stream adapter operating modes"""
    SINGLE_HASH = 0  # Hash single chunk at a time
    PIPELINE_HASH = 1  # Pipeline multiple chunks
    BATCH_HASH = 2  # Hash batch of chunks
    STREAM_HASH = 3  # Continuous stream hashing


@dataclass
class ASICChip:
    """ASIC chip configuration"""
    chip_id: str
    uart_address: int
    pll_frequency_mhz: float
    hash_rate_ghs: float  # Giga-hashes per second
    voltage_v: float  # Operating voltage
    power_w: float  # Power consumption in watts
    temperature_c: float  # Temperature in Celsius


@dataclass
class StreamChunk:
    """Chunk of data to be hashed"""
    chunk_id: int
    data: bytes
    size_bytes: int
    timestamp: float


@dataclass
class HashResult:
    """Result of hashing a chunk"""
    chunk_id: int
    hash_hex: str  # SHA-256 hash as hex string
    hash_bytes: bytes  # SHA-256 hash as bytes
    duration_ms: float
    chip_id: str


@dataclass
class StreamAdapterConfig:
    """Configuration for ASIC stream adapter"""
    chunk_size_bytes: int = 1024  # 1KB chunks by default
    pipeline_depth: int = 4  # Number of chunks in pipeline
    pll_multiplier: float = 1.0  # PLL clock multiplier
    voltage_target_v: float = 1.0  # Target voltage
    temperature_max_c: float = 80.0  # Max temperature


class NanokernelUART:
    """
    Nanokernel UART communication layer for ASIC chips.
    
    Handles low-level UART communication with ASIC chips:
    - Send TYPE 2 commands (chip commands)
    - Read/write registers
    - Control PLL clock frequency
    - Monitor chip status
    """
    
    def __init__(self, chip: ASICChip):
        self.chip = chip
        self.uart_baud = 115200  # Default baud rate
        self.uart_config = (8, 0, 1)  # 8N1
    
    def send_command(self, command_type: int, address: int, data: bytes) -> bytes:
        """
        Send UART command to ASIC chip.
        
        Command structure: 0x55 0xAA TYPE ADDRESS[2] DATA...
        Response structure: 0xAA 0x55 TYPE DATA...
        """
        # Preamble
        cmd = bytearray([0x55, 0xAA])
        # Command type
        cmd.append(command_type)
        # Address (2 bytes, big-endian)
        cmd.extend(address.to_bytes(2, 'big'))
        # Data
        cmd.extend(data)
        
        # Simulate UART transmission (in real implementation, this would be actual UART)
        # For now, return simulated response
        response = bytearray([0xAA, 0x55])
        response.append(command_type)  # Response echoes command type
        response.extend(data)  # Response echoes data (simplified)
        
        return bytes(response)
    
    def read_register(self, register_address: int) -> int:
        """Read register from ASIC chip"""
        response = self.send_command(2, register_address, b"\x00\x00")
        # Simplified: extract register value from response
        return int.from_bytes(response[4:8], 'big')
    
    def write_register(self, register_address: int, value: int) -> bool:
        """Write register to ASIC chip"""
        data = value.to_bytes(4, 'big')
        response = self.send_command(2, register_address, data)
        # Simplified: check if write succeeded
        return len(response) > 4
    
    def set_pll_frequency(self, frequency_mhz: float) -> bool:
        """Set PLL clock frequency"""
        # PLL register is at address 0x08
        # Formula: fPLL0 = fCLKI x FBDIV / (REFDIV x POSTDIV1 x POSTDIV2)
        # Simplified: write frequency value to PLL register
        pll_value = int(frequency_mhz * 1e6)
        return self.write_register(0x08, pll_value)
    
    def get_chip_status(self) -> dict:
        """Get chip status (temperature, hash rate, etc.)"""
        # Simplified: read status registers
        return {
            "temperature": self.chip.temperature_c,
            "hash_rate": self.chip.hash_rate_ghs,
            "voltage": self.chip.voltage_v,
            "power": self.chip.power_w
        }


class StreamAdapterNanokernel:
    """
    Nanokernel for ASIC stream adapter.
    
    Responsibilities:
    1. UART communication with ASIC chips
    2. Stream buffering and chunking
    3. Hash result aggregation
    4. Error handling and retry logic
    5. Clock rate control (via PLL)
    6. Power management
    
    The nanokernel exposes the ASIC as a general-purpose stream processor.
    Input: arbitrary data stream → Output: SHA-256 hash stream
    """
    
    def __init__(self, chips: List[ASICChip], config: StreamAdapterConfig):
        self.chips = chips
        self.config = config
        self.uart_layers = [NanokernelUART(chip) for chip in chips]
        self.mode = StreamAdapterMode.STREAM_HASH
        self.pipeline: List[StreamChunk] = []
        self.results: List[HashResult] = []
        self.total_chunks_hashed = 0
        self.total_bytes_hashed = 0
        self.start_time = time.time()
    
    def initialize(self) -> bool:
        """Initialize nanokernel and ASIC chips"""
        print("Nanokernel initialization...")
        
        # Initialize UART layers
        for uart in self.uart_layers:
            print(f"  Initializing UART for chip {uart.chip.chip_id}")
        
        # Set PLL frequency for all chips
        target_pll = self.chips[0].pll_frequency_mhz * self.config.pll_multiplier
        for uart in self.uart_layers:
            success = uart.set_pll_frequency(target_pll)
            print(f"  Set PLL to {target_pll:.2f} MHz: {success}")
        
        print("Nanokernel initialization complete")
        return True
    
    def chunk_stream(self, data: bytes) -> List[StreamChunk]:
        """Split data stream into chunks"""
        chunks = []
        chunk_size = self.config.chunk_size_bytes
        num_chunks = (len(data) + chunk_size - 1) // chunk_size
        
        for i in range(num_chunks):
            start = i * chunk_size
            end = min(start + chunk_size, len(data))
            chunk_data = data[start:end]
            
            chunk = StreamChunk(
                chunk_id=i,
                data=chunk_data,
                size_bytes=len(chunk_data),
                timestamp=time.time()
            )
            chunks.append(chunk)
        
        return chunks
    
    def hash_chunk(self, chunk: StreamChunk, chip_index: int = 0) -> HashResult:
        """
        Hash a single chunk using ASIC chip.
        
        In real implementation, this would:
        1. Send chunk data to ASIC via UART
        2. Wait for hash result
        3. Return hash result
        
        For simulation, we use Python's hashlib.
        """
        start_time = time.time()
        
        # Simulate ASIC hashing (in real implementation, send to ASIC)
        # Use Python's hashlib for simulation
        hash_obj = hashlib.sha256(chunk.data)
        hash_bytes = hash_obj.digest()
        hash_hex = hash_obj.hexdigest()
        
        duration_ms = (time.time() - start_time) * 1000
        
        return HashResult(
            chunk_id=chunk.chunk_id,
            hash_hex=hash_hex,
            hash_bytes=hash_bytes,
            duration_ms=duration_ms,
            chip_id=self.chips[chip_index].chip_id
        )
    
    def hash_stream(self, data: bytes) -> List[HashResult]:
        """
        Hash entire data stream using stream adapter.
        
        Process:
        1. Chunk the stream
        2. Hash each chunk (pipeline if configured)
        3. Aggregate results
        4. Return hash stream
        """
        chunks = self.chunk_stream(data)
        results = []
        
        print(f"Hashing {len(data)} bytes in {len(chunks)} chunks...")
        
        for chunk in chunks:
            # Round-robin chip selection for load balancing
            chip_index = chunk.chunk_id % len(self.chips)
            result = self.hash_chunk(chunk, chip_index)
            results.append(result)
            self.total_chunks_hashed += 1
            self.total_bytes_hashed += chunk.size_bytes
        
        self.results.extend(results)
        return results
    
    def hash_stream_pipeline(self, data: bytes) -> List[HashResult]:
        """
        Hash stream with pipeline parallelization.
        
        Pipeline depth determines how many chunks are processed in parallel.
        """
        chunks = self.chunk_stream(data)
        results = []
        
        print(f"Pipeline hashing {len(data)} bytes in {len(chunks)} chunks (depth={self.config.pipeline_depth})...")
        
        # Simplified pipeline: process chunks in batches
        batch_size = self.config.pipeline_depth
        for i in range(0, len(chunks), batch_size):
            batch = chunks[i:i + batch_size]
            batch_results = []
            
            for chunk in batch:
                chip_index = chunk.chunk_id % len(self.chips)
                result = self.hash_chunk(chunk, chip_index)
                batch_results.append(result)
                self.total_chunks_hashed += 1
                self.total_bytes_hashed += chunk.size_bytes
            
            results.extend(batch_results)
        
        self.results.extend(results)
        return results
    
    def get_statistics(self) -> dict:
        """Get stream adapter statistics"""
        elapsed_time = time.time() - self.start_time
        throughput_mbps = (self.total_bytes_hashed / 1e6) / elapsed_time if elapsed_time > 0 else 0
        
        return {
            "total_chunks_hashed": self.total_chunks_hashed,
            "total_bytes_hashed": self.total_bytes_hashed,
            "elapsed_time_seconds": elapsed_time,
            "throughput_mbps": throughput_mbps,
            "chunks_per_second": self.total_chunks_hashed / elapsed_time if elapsed_time > 0 else 0,
            "num_chips": len(self.chips),
            "mode": self.mode.name
        }
    
    def shutdown(self):
        """Shutdown nanokernel and ASIC chips"""
        print("Nanokernel shutdown...")
        
        # Reset PLL to default frequency
        for uart in self.uart_layers:
            uart.set_pll_frequency(self.chips[0].pll_frequency_mhz)
        
        print("Nanokernel shutdown complete")


# ============================================================================
# DEMONSTRATION
# ============================================================================

def demonstrate_asic_stream_adapter():
    """Demonstrate ASIC stream adapter nanokernel"""
    
    print("=" * 80)
    print("ASIC STREAM ADAPTER NANOKERNEL DEMONSTRATION")
    print("=" * 80)
    print()
    
    # Configure ASIC chips (simulated BM1397 chips)
    chips = [
        ASICChip(
            chip_id="asic_001",
            uart_address=0x00,
            pll_frequency_mhz=2400.0,
            hash_rate_ghs=50.0,
            voltage_v=1.0,
            power_w=3000.0,
            temperature_c=45.0
        ),
        ASICChip(
            chip_id="asic_002",
            uart_address=0x04,
            pll_frequency_mhz=2400.0,
            hash_rate_ghs=50.0,
            voltage_v=1.0,
            power_w=3000.0,
            temperature_c=47.0
        ),
        ASICChip(
            chip_id="asic_003",
            uart_address=0x08,
            pll_frequency_mhz=2400.0,
            hash_rate_ghs=50.0,
            voltage_v=1.0,
            power_w=3000.0,
            temperature_c=46.0
        )
    ]
    
    # Configure stream adapter
    config = StreamAdapterConfig(
        chunk_size_bytes=1024,
        pipeline_depth=4,
        pll_multiplier=1.0,
        voltage_target_v=1.0,
        temperature_max_c=80.0
    )
    
    # Initialize nanokernel
    nanokernel = StreamAdapterNanokernel(chips, config)
    nanokernel.initialize()
    print()
    
    # Test data stream
    test_data = b"This is a test data stream for the ASIC stream adapter nanokernel. " * 100
    print(f"Test data size: {len(test_data)} bytes")
    print()
    
    # Hash stream (single-threaded)
    print("MODE: SINGLE_HASH")
    print("-" * 80)
    nanokernel.mode = StreamAdapterMode.SINGLE_HASH
    results_single = nanokernel.hash_stream(test_data)
    print(f"Hashed {len(results_single)} chunks")
    for result in results_single[:3]:
        print(f"  Chunk {result.chunk_id}: {result.hash_hex[:16]}... ({result.duration_ms:.2f}ms)")
    if len(results_single) > 3:
        print(f"  ... and {len(results_single) - 3} more")
    print()
    
    # Hash stream (pipeline)
    print("MODE: PIPELINE_HASH")
    print("-" * 80)
    nanokernel.mode = StreamAdapterMode.PIPELINE_HASH
    results_pipeline = nanokernel.hash_stream_pipeline(test_data)
    print(f"Hashed {len(results_pipeline)} chunks")
    for result in results_pipeline[:3]:
        print(f"  Chunk {result.chunk_id}: {result.hash_hex[:16]}... ({result.duration_ms:.2f}ms)")
    if len(results_pipeline) > 3:
        print(f"  ... and {len(results_pipeline) - 3} more")
    print()
    
    # Statistics
    print("STATISTICS")
    print("-" * 80)
    stats = nanokernel.get_statistics()
    for key, value in stats.items():
        print(f"  {key}: {value}")
    print()
    
    # Shutdown
    nanokernel.shutdown()
    print()
    
    # Comparison with NES unified stack
    print("=" * 80)
    print("COMPARISON WITH NES UNIFIED STACK")
    print("=" * 80)
    print("""
NES Unified Stack:
- 1985 hardware → 2026 neural compression/upload tech substrate
- Controller ports → bidirectional UART
- Audio lines → DSP math computation
- Voltage levels → computational substrate
- 256×240 → 640×480 via microgrid emulation
- Key insight: Single-purpose hardware can be repurposed with creative architecture

ASIC Stream Adapter:
- SHA-256 ASIC → general-purpose stream adapter
- UART interface → stream communication
- SHA-256 cores → hash stream processor
- PLL control → throughput control
- Voltage control → power management
- Key insight: Accept hardware limitations, expose capability as stream adapter

Difference:
- NES: Repurposed existing interfaces for new computational purposes
- ASIC: Existed existing capability (SHA-256) as general stream adapter

Similarity:
- Both use nanokernel to bridge hardware to new use cases
- Both accept hardware limitations and work within them
- Both prove that "single-purpose" is a design choice, not physical limitation
""")
    
    # Use cases
    print("=" * 80)
    print("STREAM ADAPTER USE CASES")
    print("=" * 80)
    print("""
1. Password Cracking:
   - Stream of candidate passwords → SHA-256 hash stream
   - Compare against target hash
   - Real-time verification

2. Brute Force Attacks:
   - Stream of candidate values → SHA-256 hash stream
   - Parallel verification across multiple chips
   - High-throughput exploration

3. Data Integrity Verification:
   - Stream of data blocks → SHA-256 hash stream
   - Compare against known good hashes
   - Real-time corruption detection

4. Merkle Tree Construction:
   - Stream of data blocks → SHA-256 hash stream
   - Build Merkle tree from hash stream
   - Batch verification

5. Proof-of-Work Mining (Original Purpose):
   - Stream of nonces → SHA-256 hash stream
   - Find hash below target difficulty
   - But now as general stream adapter, not hardcoded to Bitcoin

Key Insight:
The ASIC stream adapter doesn't try to make the ASIC do something other than SHA-256.
It accepts that the ASIC can only do SHA-256 and exposes this as a general-purpose
stream processor. This is the same principle as the NES unified stack: work within
hardware limitations, don't fight them.
""")


if __name__ == "__main__":
    demonstrate_asic_stream_adapter()