Unified Code Generation for Parallel Network Hardware
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Solution Overview
Problem
Existing approaches to developing parallel networks, such as neural networks, often require separate specifications for general-purpose programming languages and hardware-specific implementations, leading to inefficiencies and errors due to the need for distinct syntactic descriptions, which complicates the integration of general-purpose language code with network elements.
Innovation Solution
The method involves generating machine executable instructions for parallel networks using a combination of general-purpose language code and network description code, where an evaluation of code elements determines the generation of instructions, allowing for unified execution of network operations through a computerized processing apparatus, enabling sensory data processing with cooperative processing elements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If separate specifications are used for general-purpose programming languages and hardware-specific implementations, then flexibility and ease of programming are improved, but integration complexity and error rates increase
Solution Approach 1:
The patent merges general-purpose language code with network description code into a unified code structure. The evaluation module analyzes code elements and determines whether they correspond to general-purpose language portions or network description portions, then generates appropriate machine executable instructions for each type. This unification eliminates the need for separate specifications and reduces integration complexity while maintaining programming flexibility.
Solution Approach 2:
The system creates a universal code generation approach where a single unified code can serve multiple purposes: it can be processed as general-purpose language code for conventional computation and as network description code for parallel network configuration. The evaluation module dynamically determines the appropriate processing path for each code element, enabling one code base to control both conventional and parallel processing without requiring separate specification languages.
2Ease of manufacture
If unified code generation is used for parallel networks, then development simplicity is improved, but processing overhead increases
Solution Approach 1:
The unified code is segmented into distinct code elements during evaluation. The evaluation module identifies whether each code element corresponds to general-purpose language portions or network description portions. This segmentation allows the system to process only the necessary portions through the evaluation and code generation pipeline, reducing overall processing overhead while maintaining development simplicity through unified code structure.
Solution Approach 2:
The system applies partial action by selectively processing only those code elements that require evaluation and transformation into machine executable instructions. General-purpose language portions that can be directly compiled or interpreted are processed through standard pathways, while only the network description portions undergo the additional evaluation and code generation steps, minimizing processing overhead.
Data Source
AI summary
Apparatus and methods for developing parallel networks. Parallel network design may comprise a general purpose language (GPC) code portion and a network description (ND) portion. GPL tools may be utilized in designing the network. The GPL tools may be configured to produce network specification language (NSL) engine adapted to generate hardware optimized machine executable code corresponding to the network description. The developer may be enabled to describe a parameter of the network. The GPC portion may be automatically updated consistent with the network parameter value. The GPC byte code may be introspected by the NSL engine to provide the underlying source code that may be automatically reinterpreted to produce the hardware optimized machine code. The optimized machine code may be executed in parallel.


