Parallel Neural Processor Architecture for Complex AI Processing
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Solution Overview
Problem
Conventional AI systems are limited by the speed of traditional processors, incur large overhead, and are unable to achieve rapid responses to complex problem sets, lacking the ability to emulate human consciousness due to serial processing and predefined boundary conditions.
Innovation Solution
A parallel neural processor architecture that implements hardware neurons capable of parallel processing, incorporating neuroplasticity and implicit memory to facilitate self-learning and intuition, utilizing weighted input modules and comparison modules to process input signals efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If conventional serial processors are used for AI applications, then system complexity is reduced and ease of manufacture is improved, but processing speed and response time deteriorate
Solution Approach 1:
The patent segments the neural network processing into multiple independent processing elements that operate in parallel. Each processing element handles specific neurons or computational units, allowing simultaneous evaluation of multiple neuron states rather than sequential processing. This segmentation enables the system to achieve high-speed parallel processing while managing complexity through modular design.
Solution Approach 2:
The patent transitions from serial (one-dimensional) processing to parallel (multi-dimensional) processing by organizing processing elements in spatial arrays. Multiple processing elements are arranged to handle different portions of the neural network simultaneously, adding a spatial dimension to the processing architecture that enables concurrent execution of multiple computational tasks.
2Adaptability or versatility
If larger neural networks are implemented to solve more complex problems, then problem-solving capability is improved, but processing time and computational overhead increase
Solution Approach 1:
The patent divides large neural networks into smaller processing blocks that can be evaluated simultaneously. Each processing element handles a subset of neurons, allowing the entire network to be processed in parallel rather than sequentially. This segmentation enables complex problem-solving capabilities while reducing overall processing time through concurrent evaluation.
Solution Approach 2:
The patent implements preliminary organization of neural network data and processing elements to enable immediate parallel evaluation. By pre-configuring the processing architecture and data structures, the system can rapidly process complex neural networks without incurring significant computational overhead during execution.
3Productivity
If conventional processors with larger core counts are used to handle larger neural networks, then processing capability is improved, but system cost and complexity increase
Solution Approach 1:
The patent segments the neural network processing function across multiple simple processing elements rather than using fewer complex cores. Each processing element is designed for specific neural network operations, enabling high productivity through parallel execution while maintaining simplicity in individual element design and reducing overall system complexity.
4Adaptability or versatility
If predefined boundary conditions are imposed on AI systems, then system simplicity and ease of operation are improved, but adaptability to different applications deteriorates
Solution Approach 1:
The patent designs processing elements with universal functionality that can handle different neural network configurations and applications. The processing architecture is designed to be application-agnostic, allowing the same hardware to efficiently process various types of neural networks for different problems without requiring complex reconfiguration or predefined boundary conditions.
Data Source
AI summary
Systems and/or devices for efficient and intuitive methods for implementing artificial neural networks specifically designed for parallel AI processing are provided herein. In various implementations, the disclosed systems, devices, and methods complement or replace conventional systems, devices, and methods for parallel neural processing that (a) greatly reduce neural processing time necessary to process more complex problem sets; (b) implement neuroplasticity necessary for self-learning; and (c) introduce the concept and application of implicit memory, in addition to explicit memory, necessary to imbue an element of intuition. With these properties, implementations of the disclosed invention make it possible to emulate human consciousness or awareness.