Multi-Terminal Logic Gate Neural Network for Real-Time AI Inference
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Solution Overview
Problem
Current computing architectures, such as CPUs, GPUs, and FPGAs, are inadequate for efficiently processing large sensor data streams in real-time due to the von Neumann bottleneck, limiting computational throughput and energy consumption, and existing neural networks are hindered by their sequential nature and power requirements, making them unsuitable for applications like autonomous vehicles.
Innovation Solution
A digital neural network design using multi-terminal logic gates and logical connectors, implemented on a single chip, which can be trained using integer linear programming to achieve real-time evaluation and inference with negligible power consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional neural networks are implemented on CPUs, GPUs, or FPGAs, then computational capability is provided, but the von Neumann bottleneck limits computational throughput and increases energy consumption
Solution Approach 1:
The patent merges the neural network computation and data storage functions into a single integrated circuit. The neural network is implemented using logic gates and logical connectors that are physically embedded within the same chip, eliminating the need for separate memory units and data transfer pathways. This integration directly resolves the von Neumann bottleneck by allowing computation and data access to occur simultaneously without sequential data movement limitations.
Solution Approach 2:
The neural network is segmented into multiple layers of logic gates with logical connectors between them, creating a distributed computational architecture. Each layer processes specific portions of the data independently, enabling parallel computation that increases throughput. The segmentation allows different parts of the network to operate simultaneously, improving productivity while reducing the energy required for sequential data processing.
2Speed
If neural networks process large sensor data streams in real-time, then responsiveness is improved, but computational complexity and power requirements increase
Solution Approach 1:
The patent replaces traditional sequential neural network computation with a parallel logic gate-based system. Instead of processing data step-by-step through sequential operations, the network uses logic gates that can evaluate multiple conditions simultaneously and logical connectors that establish parallel computational paths. This substitution enables real-time processing of large datasets by performing computations in parallel rather than sequentially, reducing the time required while managing complexity through structured logic architecture.
3Adaptability or versatility
If existing neural networks are used, then AI processing capability is provided, but power consumption is high and sequential nature limits efficiency
Solution Approach 1:
The patent implements a dynamic neural network architecture where logical connectors can be configured in different states (e.g., connected, disconnected, inverted) to adapt the computational graph. This dynamic reconfigurability allows the network to adjust its structure based on the specific computational task, enabling efficient processing of diverse AI functions. The dynamics of the logical connectors enable the system to optimize power consumption by activating only the necessary computational paths required for each specific AI processing task.
Data Source
AI summary
A deep neural network circuit with multiple layers formed of multi-terminal logic gates is provided. In one aspect, the neural network circuit includes a plurality of logic gates arranged into a plurality of layers and a plurality of logical connectors arranged between each pair of adjacent layers. Each of the logical connectors connects the output of a first logic gate to the input of a second logic gate and each of the logical connectors has one of a plurality of different logical connector states. The neural network circuit is configured to be trained to implement a function by finding a set of the logical connector states for the logical connectors such that the neural network circuit implements the function.