SystemC Over-Engineered Space Invaders Simulator

A hardware-software co-design implementation of the classic Space Invaders game using SystemC for system-level modeling and simulation. This project demonstrates transaction-level modeling (TLM), hardware acceleration, neural network processing, and power modeling in a SystemC environment.

Screenshot

Architecture Overview

The system implements a modular architecture using SystemC TLM sockets for inter-module communication:

GameTop (Top-level module)
├── CPU (Game logic processor)
├── Bus (TLM interconnect)
├── Display (SDL2 rendering)
├── Input (Keyboard handling)
├── Memory (System memory)
├── Accelerator (Collision detection HW)
├── NNAccelerator (Neural network processing)
└── PowerModel (Power consumption modeling)

Core Components

• CPU (modules/cpu.{h,cpp}): Main game logic processor implementing Space Invaders gameplay, including player movement, bullet physics, collision detection, and game state management
• Bus (modules/bus.{h,cpp}): TLM-based interconnect routing transactions between CPU and peripheral modules using address decoding
• Display (modules/display.{h,cpp}): SDL2-based graphics renderer with command queue processing for draw and text operations
• Input (modules/input.{h,cpp}): Keyboard input handler with interrupt generation capabilities
• Accelerator (modules/accelerator.{h,cpp}): Hardware accelerator for collision detection and row boundary calculations with parallel processing
• NNAccelerator (modules/nn_accelerator.{h,cpp}): Neural network processing unit implementing MLP for AI-based game behavior
• Memory (modules/memory.{h,cpp}): System memory module with TLM interface
• PowerModel (modules/power_model.{h,cpp}): Dynamic power consumption modeling and DVFS control

Key Features

• Transaction-Level Modeling: TLM 2.0 sockets for communication between modules
• Hardware Acceleration: Dedicated accelerator for computationally intensive collision detection
• Neural Network Integration: On-chip MLP for adaptive gameplay mechanics
• Power Modeling: Dynamic power tracking with frequency scaling simulation
• Quantum Random Number Generation: QRNG implementation for enhanced randomness
• Debug Support: Comprehensive debug mode with performance metrics

Implementation Details

Memory Map

• Input Controller: 0x1000
• Display Controller: 0x2000
• Power Manager: 0x3000
• Accelerator: 0x4000
• System Memory: 0x5000-0x6FFF
• NN Accelerator: 0x6000

Game Mechanics

• 5x11 invader formation with row-based movement patterns
• Destructible bunkers with pixel-level damage tracking
• Multi-bullet system (100 player, 200 invader bullets max)
• Adaptive difficulty through neural network-based speed control

Hardware Acceleration

The collision detection accelerator processes invader rows in parallel, computing:

• Row boundary calculations (min/max positions)
• Bullet-invader collision detection
• Bunker collision and damage modeling

Dependencies

Required Libraries

• SystemC 2.3+: System-level modeling framework
• SDL2: Graphics and input handling
• SDL2_ttf: Text rendering support
• SDL2_mixer: Audio support (linked but optional)

System Requirements

• C++17 compatible compiler (GCC recommended)
• POSIX-compliant system (Linux/macOS)
• SystemC installation with TLM support

Installation

Ubuntu/Debian

sudo apt-get install libsystemc-dev libsdl2-dev libsdl2-ttf-dev libsdl2-mixer-dev

Building from Source

1. Install SystemC from https://systemc.org
2. Install SDL2 development libraries
3. Set SYSTEMC_HOME environment variable if needed

Build Instructions

make clean
make all

Available Targets

• make all: Build the executable
• make run: Build and run the simulation
• make debug: Build and run with debug output
• make clean: Remove build artifacts

Usage

Basic Execution

./space_invaders

Debug Mode

./space_invaders -debug

Debug mode provides detailed logging of:

• SystemC simulation events
• TLM transaction details
• Performance metrics
• Power consumption data

Controls

• Left/Right Arrow: Move player
• Spacebar: Fire bullets
• ESC: Quit game

Performance Considerations

The simulator includes several performance optimizations:

• TLM Direct Memory Interface (DMI) for high-speed memory access
• Parallel collision detection using hardware acceleration
• Optimized SDL2 rendering with command queues
• Quantum keeper for SystemC simulation performance

Technical Notes

• Uses TLM 2.0 blocking transport for simplicity and determinism
• Implements approximate timing for realistic hardware modeling
• Neural network training occurs during gameplay for adaptive behavior
• Power modeling includes both static and dynamic components
• QRNG provides quantum-inspired randomness for enhanced gameplay variety

License

This project is intended for educational and research purposes, demonstrating SystemC-based hardware-software co-design methodologies.