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How to Build Your Local Propagation Network
- The concept of local propagation network was introduced in the context of solving math problems using constrained random solver in SystemVerilog.
- Inspired by Mark Jason Dominus's approach in Higher Order Perl, the network breaks down formulas into nodes and edges for arithmetic operations.
- The network consists of I/O nodes for inputs, constant nodes for fixed values, and operator nodes for arithmetic calculations.
- Bidirectional connections between nodes and smart operator nodes capable of forward and backward calculations are crucial for the network's functionality.
- In SystemVerilog, the implementation of the local propagation network differs from Dominus's Perl approach, focusing on modular and hierarchical constructs.
- The network uses real values for temperatures, implemented as 64-bit integral variables driven by conversion functions.
- Operator nodes like adder and multiplier handle arithmetic operations, with logic to enable driving values and calculate results.
- I/O nodes interact with the user to set values, completing the network that solves linear relationships like temperature conversions.
- This Verilog-based network resembles neurons in the brain, hinting at the potential for neural network-like systems and artificial intelligence.
- The implementation showcases the power of Verilog constructs in creating computational circuits and solving constraints through interconnected nodes.
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