Cognitive Allocation of Specialized Hardware from Code Context
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Solution Overview
Problem
Existing cloud computing systems inefficiently allocate specialized hardware resources, leading to unnecessary expense and inactive hardware locking, which results in inefficient use and the need for additional hardware purchases.
Innovation Solution
A cognitive allocation mechanism that analyzes source code context using natural language processing (NLP) and a custom knowledge base to identify tasks requiring specialized hardware, predicting time allotments and optimizing resource allocation through a hardware scheduler.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If specialized hardware resources are allocated to computing nodes, then computing capability and processing power are improved, but hardware cost and resource waste increase due to inactive hardware locking
Solution Approach 1:
The system performs preliminary analysis of code segments using NLP and neural networks to predict hardware requirements before actual execution. This allows the hardware scheduler to allocate specialized resources in advance based on predicted needs rather than actual usage patterns, preventing both over-allocation and under-allocation.
Solution Approach 2:
The system implements feedback mechanisms where actual hardware usage is monitored and fed back into the neural network models. This continuous feedback loop allows the system to refine its predictions and improve allocation accuracy over time, reducing waste while ensuring adequate resource provision.
2Ease of operation
If hardware resources are allocated based on general requirements, then ease of operation is improved, but manufacturing precision and resource allocation accuracy deteriorate
Solution Approach 1:
The system introduces an intermediary layer of code segment analysis that acts as a mediator between user requests and hardware allocation. The NLP-based code analyzer and neural network models process code segments to determine precise hardware requirements, enabling accurate allocation while maintaining user-friendly operation through automated analysis.
3Manufacturing precision
If code analysis is performed manually, then manufacturing precision of resource allocation is improved, but productivity and processing time deteriorate
Solution Approach 1:
The system replaces manual code analysis with automated NLP-based neural network models. These models process code segments, identify hardware requirements, and predict execution characteristics automatically, achieving both high accuracy in allocation decisions and improved processing speed through computational automation.
4Reliability
If specialized hardware is allocated for entire code execution, then reliability is improved, but loss of time and resource efficiency deteriorate
Solution Approach 1:
The system segments code into individual code segments and analyzes each segment's hardware requirements independently. This segmentation allows the hardware scheduler to allocate specialized resources only for the specific time periods when particular code segments need them, rather than allocating for the entire execution duration, thereby reducing idle time while maintaining reliability.
Data Source
AI summary
An embodiment includes tokenizing a code segment generated by an integrated development environment responsive to user input, the tokenizing resulting in a tokenized code segment. The embodiment generates a search query by analyzing the tokenized code segment using a trained neural network. The embodiment executes a querying process that searches a knowledge base using the search query for a hardware requirement associated with the code segment, and detects a search result that conveys the hardware requirement as including a specialized hardware resource. The embodiment generates a time allotment associated with execution of the code segment using a second trained neural network that predicts the time allotment based on the tokenized code segment. The embodiment issues an instruction associated with the code segment to a hardware scheduler causing the hardware scheduler to allocate the specialized hardware resource for an amount of time corresponding to the time allotment.