Non-Binary Logic for Efficient AI Acceleration
Simulating balanced ternary arithmetic to reduce circuit complexity and engineering rigorous protocol toolkits.
Overview
I investigated whether non-binary computation could reduce the transistor count and energy cost of AI inference. I built Python simulations of balanced ternary logic, implementing a half-adder model to document theoretical efficiency bounds for specialized accelerator designs. In parallel, I developed a protocol toolkit in Zig featuring canonical transaction encoding, signing-hash generation, and signature verification. This toolkit achieved live RPC conformance and deterministic serialization for a declared protocol subset, validated through five quality gates including cross-implementation parity. Both efforts reinforce a focus on verifiable, low-level systems that apply formal rigor to next-generation computing architectures.
Highlights
- 01
Balanced ternary half-adder simulation with documented LLM efficiency gains
- 02
Zig protocol toolkit: deterministic serialization and live RPC conformance
- 03
Five quality gates including cross-implementation parity and testnet verification
System Architecture
Simulation workflow for ternary logic and validation pipeline for deterministic serialization.
Questions people ask
- How does balanced ternary logic improve AI acceleration?
- It reduces transistor count and energy costs by simulating a half-adder model that establishes theoretical efficiency bounds for specialized accelerators.
- What capabilities does the Zig protocol toolkit provide?
- The toolkit features canonical transaction encoding, signing-hash generation, and signature verification with deterministic serialization.
- How was the Zig toolkit validated?
- It achieved live RPC conformance and passed five quality gates, including cross-implementation parity and testnet verification.