Beyond Binary: Ternary Logic Shapes Next-Gen AI Hardware, Led by Drones

By Patrick Seaman | CEO @ SportsBug™, Author of Streaming Wars

Ternary logic is quietly shaping the next wave of AI hardware. Drones will benefit first.

Binary computing has carried AI an incredible distance, but we’re hitting walls. The toughest AI workloads still slam into power constraints, latency bottlenecks, and uncertainty gaps that binary simply wasn’t designed to handle elegantly.

Enter ternary logic, which uses three symbols instead of two. What started as academic theory is now moving into practical circuits and model compression. The payoff is compelling: simpler arithmetic operations, dramatically fewer memory movements, and native support for encoding “unknown” states. That last piece matters enormously when machines must make safe decisions in unpredictable real-world environments.

The 2024-2025 breakthrough moment

Three major shifts converged to make ternary computing practical:

Ternary AI models moved from lab curiosities to production candidates. Researchers demonstrated that large language models and vision-language models can be ternarized, replacing traditional weights with values from just three options: {-1, 0, +1}. The results preserved task quality while delivering massive cuts in multiply operations and memory bandwidth usage. Both are primary energy consumers and cost drivers at the edge, making this breakthrough commercially significant.

Hardware finally caught up with the theory. New device research shows working circuits that dynamically switch between binary and ternary modes on the same CMOS inverter. We’re seeing full ternary logic gates and arithmetic units built with emerging materials like graphene nanoribbon FETs and carbon nanotube FETs. These aren’t just simulations anymore. Multiple research groups have demonstrated functional adders, multipliers, and logic gates with measurable power-delay advantages over traditional binary approaches.

System-level architecture thinking arrived. The missing piece was always how to build complete systems around ternary computing. Now we have teams publishing queuing models, scheduling algorithms, and even ternary optical computer architectures that intelligently trade performance against energy consumption. This foundational work is essential before anyone can build real production systems and software stacks.

Why drones are the perfect testing ground

Autonomous drones operate in exactly the conditions where ternary computing shines. They work with partial information from noisy sensors while operating under strict compute and power budgets. Ternary offers two critical advantages here.

Uncertainty belongs directly in the logic itself. Three-valued semantics already appear naturally in robotics planning and temporal logic frameworks. A sensor reading or environmental condition can be true, false, or genuinely unknown until additional evidence becomes available. This maps perfectly to ternary encodings in perception and planning systems, reducing costly recomputation cycles and enabling more conservative, safer decision-making when data is ambiguous or conflicting.

Energy and latency directly determine mission success. Every watt matters in small airframes, and every millisecond counts in control loops. Ternary models slash the number of multiply-accumulate operations required for inference. Ternary circuits create shorter logical paths for critical arithmetic operations like addition and multiplication. Recent device-level results show concrete reductions in power-delay product that translate directly to longer flight times, faster onboard inference, and more reliable autonomy without depending on cloud connectivity.

The implications extend beyond individual flights. In contested environments or urban airspace, the ability to process more sensor data locally while consuming less power could determine whether autonomous systems can operate effectively or must retreat to simpler, less capable modes.

Practical steps to prepare now

The transition to ternary won’t happen overnight, but the foundation is being laid today. Here’s how to position yourself:

Start experimenting with ternary-friendly quantization in your model development pipeline. Begin with ternary weight experiments on edge-focused tasks where power and latency matter most. Follow the emerging literature on LLM and vision-language model ternarization for proven training recipes and optimized operator kernels.

Prototype using reconfigurable hardware while monitoring device research. FPGA emulation provides a practical bridge for experimentation today. But keep watching the device papers closely. The real goal is application-specific integrated circuits or mixed-signal processing blocks that can dynamically switch between binary and ternary modes as workloads change throughout operation.

Adopt planning frameworks that explicitly acknowledge unknowns. Implement three-valued temporal logic in your planning systems now. This approach pairs naturally with ternary representations in perception systems, and it provides a much safer default behavior pattern for operations in urban environments or contested airspace where assumptions can be deadly.

Build partnerships across the stack. The shift to ternary computing will require coordination between model developers, hardware designers, and system integrators. No single company can drive this transition alone.

The realistic outlook

Binary computing will remain dominant for the foreseeable future. It provides the foundation for current CPUs, GPUs, and memory architectures that power today’s AI systems. Any transition to ternary computing will be gradual, selective, and driven by specific application requirements rather than wholesale technology replacement.

Think of this in comparison to gasoline stations in the United States versus electric car charging stations. It took about 100 years to build approximately 145,000 gas stations nationwide. In contrast, electric charging infrastructure has grown from around 2,000 stations to roughly 75,000+ in just 15-20 years. Technology transitions don’t happen overnight, but they can accelerate once momentum builds.

By some estimates, approximately 6–10 trillion binary integrated circuits have been produced worldwide since the 1970s, representing enormous sunk investment in manufacturing processes, design tools, and engineering expertise. While ternary devices will likely emerge as a growing segment, the high demand placed on existing fabs to produce current-generation chips means it may be considerable time before ternary circuits move beyond specialized research applications.

The current research momentum suggests ternary computing deserves attention from technology strategists, but expectations should remain measured. Laboratory demonstrations, while promising, represent early-stage research rather than near-term product development.

Applications where uncertainty handling and extreme energy efficiency dominate design constraints could see experimental ternary implementations first. Autonomous systems, edge AI, and specialized processing tasks operating under severe resource limitations represent the most likely initial deployment scenarios. However, even in these areas, practical systems likely require years of additional development.

The research pipeline shows genuine progress on both algorithmic and hardware fronts. Multiple academic groups are publishing peer-reviewed results, major institutions are funding related research, and the theoretical foundations are becoming more solid. This represents the kind of sustained scientific momentum that often precedes significant technological shifts.

For organizations developing long-term technology strategies, ternary computing warrants inclusion in scenario planning and research monitoring. The potential benefits are substantial enough to justify tracking, even if practical deployment remains distant. Early preparation could provide competitive advantages when and if these technologies mature.

The key insight is recognizing inflection points while they’re still developing rather than waiting for market validation. Ternary computing may or may not achieve widespread adoption, but the current research trajectory suggests it has moved beyond pure academic curiosity toward engineering viability studies.

References

• Chen, T. et al. (2024). TernaryLLM: Ternarized Large Language Model. https://arxiv.org/pdf/2406.07177
• Convertible T-CMOS inverter with binary/ternary modes (2025). Advanced Functional Materials. https://advanced.onlinelibrary.wiley.com/doi/10.1002/adfm.202510164
• Zhao, G. et al. (2024). Efficient Ternary Logic Circuits Optimized by Ternary Evolution. https://hajim.rochester.edu/ece/sites/friedman/papers/TEmerging_24.pdf
• Abbasian, E. et al. (2024). A highly-efficient ternary-capable GNRFET-based three-valued half-adder. https://www.sciencedirect.com/science/article/abs/pii/S0921510724002812
• Design of GNRFET ternary circuits using pass transistor logic (2025). https://pubs.aip.org/aip/acp/article-lookup/doi/10.1063/5.0249485

• Zhang, H. et al. (2025). Performance and energy optimization of ternary optical computers. Scientific Reports. https://www.nature.com/articles/s41598-025-00135-x
• Also available via Springer Nature open posting: https://www.researchgate.net/publication/391277339_Performance_and_energy_optimization_of_ternary_optical_computers_based_on_tandem_queuing_system

• Zhang, S. et al. (2024). Dual-center programming model for ternary optical computing. Scientific Reports. https://www.nature.com/articles/s41598-024-75976-z

• ADS record: https://ui.adsabs.harvard.edu/abs/2024NatSR..1424696Z/abstract
• Multi-Robot LTL Planning under Uncertainty using three-valued semantics (survey notes). https://www.cse.chalmers.se/~bergert/roboticssurvey/fm.pdf
• Ternarization of Vision-Language Models for edge deployment (2025). https://arxiv.org/html/2504.06298v1