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Hardware simulation

T81.hardware.TernaryEmulator lets you sketch ternary chips, evaluate fuzzy decisions, and estimate energy costs for AI edge deployments. It mirrors the core library by keeping wires as t81lib.Limb values so trit-level bookkeeping stays consistent with the rest of the stack.

Highlights include:

  • visualize_circuit() for matplotlib-friendly diagrams with optional Graphviz exports so you can illustrate ternary datapaths or neuromorphic operators.
  • Fuzzy helpers such as fuzzy_and, fuzzy_decision, and fuzzy_not so agents can reason about “false/maybe/true” beliefs before dispatching ternary or binary schedules.
  • simulate_torch_forward() plus power-tracing hooks that tally trit flips, emulate hybrid forward passes, and let you compare ternary energy to binary energy budgets.

See examples/ternary_hardware_sim_demo.ipynb for a guided walkthrough that builds a ternary adder, runs a small inference, records virtual power/latency metrics, and highlights how balanced ternary drops switching energy for drones or tiny neuromorphic chips.

The hardware kernel sketch summarizes the tryte packing → multiply → accumulate flow used by the AVX/NEON GEMM paths.