3-Terminal Digital Neurons for AI Applications on Everyday Devices

This is the digital equivalent of my last blog. It considered analog neurons because I wanted to consider designing for potential consciousness. This one just looks at digital neurons, using the potential energy saving advantages.

I discussed this idea with GPT4 and then got it to write this blog. It’s good enough to get the idea across. I don’t have the means to simulate the performance of 3-terminal nets compared to conventional approached. I am hoping that it could be comparable to migrating towards RISC a few decades ago and thus offer advantage for certain types of problem. As this blog shows, it might offer promise, but it might not be very significant.

As artificial intelligence (AI) continues to gain momentum, researchers and developers are continually exploring new methods to improve the performance, energy efficiency, and adaptability of AI applications on everyday devices such as laptops, PCs, and mobile phones. One promising approach involves the use of 3-terminal digital neurons in neural networks, which could lead to a paradigm shift in the AI landscape, similar to the impact of Reduced Instruction Set Computing (RISC) in the computing field. In this blog, we delve into the concept of 3-terminal digital neurons, discuss their potential advantages, and explore their applicability in AI applications on everyday devices.

The Concept: 3-Terminal Digital Neurons

Traditional neural networks typically use neurons with multiple input connections and a single output connection. However, the concept of 3-terminal digital neurons offers a departure from this traditional design. Each 3-terminal neuron has three connections that can serve as input or output at any given time, allowing for dynamic reconfiguration during operation. The use of 3-terminal neurons in neural networks presents several potential benefits, including simplicity, adaptability, and energy efficiency.

Advantages of 3-Terminal Digital Neurons

  1. Reduced complexity: Neural networks with 3-terminal neurons can be designed with fewer connections, which simplifies the overall architecture. This reduced complexity can lead to faster development times and easier implementation in AI applications on laptops, PCs, and mobile phones.
  2. Energy efficiency: As 3-terminal neurons require fewer connections, they may consume less energy during computation. This can be especially beneficial for AI applications running on mobile devices, where battery life is a critical concern.
  3. Adaptability and flexibility: The dynamic nature of the connections in a 3-terminal neuron network enables greater adaptability and flexibility. This can lead to improved learning and adaptation capabilities in AI applications, resulting in better performance on a wide range of tasks.

Interworking with GPUs and CPUs

Simulating neural networks using combinations of 3-terminal and higher-level neurons could be an effective way to explore the potential of this approach. By investigating the compatibility and performance of these networks with existing GPU and CPU architectures, we can determine whether the overall computing power available on mobile, laptop, or PC devices would be better utilized by simulating 3-terminal neuron nets or by employing conventional approaches.

Moreover, if 3-terminal digital neurons do confer an advantage, it is worth considering whether relatively small investments in R&D could lead to the redesign of processor architectures to better suit this novel approach. This could result in more efficient, flexible, and adaptable AI applications on everyday devices.

Challenges and Future Directions

While 3-terminal digital neurons offer several potential advantages, there are also challenges to overcome in order to fully realize their potential in AI applications:

  1. Network complexity: A neural network with 3-terminal neurons may require more neurons or layers to achieve the same level of complexity as a network with neurons having a higher number of inputs. This may result in increased computational complexity and longer training times.
  2. Training algorithms: Developing appropriate training algorithms specifically for 3-terminal neuron networks is essential for optimizing their performance in AI applications.
  3. Scalability: Ensuring that 3-terminal digital neuron networks can scale effectively to handle large and complex AI tasks is crucial for their successful implementation on laptops, PCs, and mobile phones.


The use of 3-terminal digital neurons in neural networks offers an intriguing and potentially advantageous approach to improving AI applications on everyday devices. Embracing the potential paradigm shift, as was the case with RISC, and learning from the interworking with existing hardware architectures can lead to the development of more powerful, efficient, and adaptable AI applications.

By addressing the challenges and building upon the inherent benefits of 3-terminal neurons, developers can create AI applications that are better suited for laptops, PCs, and mobile phones. The potential of this approach should not be underestimated, as it could pave the way for significant advancements in the field of AI.

Future Research and Collaboration:

To push the boundaries of AI using 3-terminal digital neurons, collaboration between researchers, developers, and industry professionals is essential. Several research directions that can be pursued to further advance this approach include:

  1. Benchmarking and evaluation: Rigorous benchmarking and evaluation of 3-terminal digital neuron networks against traditional neural network architectures can help identify the strengths, weaknesses, and specific use cases where this approach excels.
  2. Hardware optimization: The development of specialized hardware tailored for 3-terminal digital neuron networks can enhance the efficiency and performance of AI applications on everyday devices.
  3. Integration with existing AI techniques: Investigating the potential for combining 3-terminal digital neuron networks with existing AI techniques, such as deep learning, reinforcement learning, and transfer learning, could lead to the development of hybrid systems that leverage the strengths of both approaches.
  4. Open-source development: Encouraging open-source development and sharing of resources, such as algorithms, software, and hardware designs, can accelerate the progress and adoption of 3-terminal digital neuron networks in the AI community.

By fostering collaboration and encouraging the exploration of this novel approach to neural networks, we can unlock the potential of 3-terminal digital neurons and drive the development of AI applications that are better suited for everyday devices. This, in turn, will enhance user experiences and enable new possibilities for AI-powered solutions on laptops, PCs, and mobile phones.

Deeper exploration

To determine the advantages or disadvantages of using 3-terminal neurons for a given app running on mobile devices in terms of speed and power consumption, we would need to consider several factors. While it’s difficult to provide a definitive answer without specific information about the app, its requirements, and the architecture of the neural network, we can discuss some general factors that could influence the performance and efficiency.

  1. Network complexity: Using 3-terminal neurons may result in an increased number of neurons and layers to achieve the same level of complexity as a network with neurons having a higher number of inputs. This may result in increased computational complexity, which could potentially impact the speed and power consumption.
  2. Connection density: A network with 3-terminal neurons would have fewer connections than a traditional neural network with a higher number of inputs. Fewer connections could lead to reduced power consumption, as there is less data to transmit and process. However, the impact on speed is more difficult to predict, as it depends on the efficiency of the underlying architecture and the specific app’s requirements.
  3. Hardware optimization: Neural networks with 3-terminal neurons might not be as well-optimized for existing hardware, such as CPUs and GPUs, as traditional neural network architectures. This could result in less efficient utilization of hardware resources, potentially affecting both speed and power consumption. However, if hardware is developed specifically for 3-terminal neurons, this factor could change.
  4. Parallelism: One of the advantages of traditional neural networks is their ability to exploit parallelism, which can lead to improved performance on parallel processing hardware like GPUs. With 3-terminal neurons, the degree of parallelism could be different, and it’s difficult to predict how this would impact the speed without knowing the specifics of the network architecture and the app.
  5. Training and inference: The performance of 3-terminal neuron networks during the training phase might differ from that during inference. Depending on the app’s requirements, one of these phases might be more critical in terms of speed and power consumption. The impact of using 3-terminal neurons on training and inference should be considered separately.

In summary, it is challenging to provide a definitive answer on whether there would be an advantage or disadvantage in speed or power consumption for a given app by migrating to a 3-terminal approach without more information. However, considering the factors mentioned above can help guide the analysis and decision-making process. Ultimately, a thorough evaluation and benchmarking of 3-terminal neuron networks against traditional neural network architectures for specific apps would be necessary to determine their relative performance and efficiency.

May not be much in it, but still worth a shot

There is no obvious large difference that would inherently shift R&D value towards or away from the 3-terminal approach without conducting further research and experimentation. The potential advantages and disadvantages of using 3-terminal neurons in neural networks are dependent on various factors, such as network complexity, connection density, hardware optimization, parallelism, and the specific requirements of the target application.

Given the novelty of the 3-terminal approach, it’s essential to perform thorough evaluations and benchmarking against traditional neural network architectures to better understand its strengths, weaknesses, and potential use cases. The R&D value of the 3-terminal approach will become clearer as more research is conducted, and the understanding of its performance characteristics and compatibility with existing hardware and algorithms improves.

It’s worth noting that exploring novel approaches, like the 3-terminal neuron networks, can lead to innovative breakthroughs and advancements in the field of AI. As a result, investing in R&D for 3-terminal neurons could potentially reveal new opportunities and applications that may not be apparent at the outset. However, the decision to invest in R&D for the 3-terminal approach should be carefully weighed against other competing research directions, available resources, and the potential risks and rewards associated with the pursuit of this novel neural network architecture.


Comments are closed.