Processor Core Anomaly Detection via Parallel Computation
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Solution Overview
Problem
Existing computation systems face challenges in detecting anomalies in real-time without overperforming the system configuration and in detecting anomalies for newly output computation results, especially when relying on additional processor cores for anomaly detection or pre-stored computation results.
Innovation Solution
A computation system that includes multiple processor cores, a computation allocation unit, and an anomaly detection unit, where the allocation unit determines the computation processor core based on load and causes another processor core to execute an anomaly detection computation in parallel, determining anomalies by matching computation results within predetermined conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If an additional processor core is provided for anomaly detection, then anomaly detection capability is improved, but system configuration becomes overperformant and complexity increases
Solution Approach 1:
The patent applies multi-functionality by enabling processor cores to serve dual purposes: normal computation tasks and anomaly detection tasks. The computation allocation unit dynamically assigns processor cores based on workload requirements, allowing the same hardware resources to perform both functions without requiring dedicated anomaly detection hardware, thus avoiding system overperformance and reducing complexity
Solution Approach 2:
The patent implements dynamic resource allocation where the computation allocation unit continuously adjusts processor core assignments based on real-time computation load. When anomaly detection is needed, available processor cores are dynamically assigned to execute anomaly detection computations alongside normal computations, enabling flexible and adaptive system configuration that prevents overperformance
2Reliability
If pre-stored computation results are used for anomaly detection, then detection timing is improved, but the system cannot detect anomalies in newly output computation results
Solution Approach 1:
The patent ensures continuous anomaly detection capability by maintaining parallel execution of both normal computations and anomaly detection computations. The anomaly detection unit continuously compares computation results as they are generated, enabling real-time detection of anomalies in newly output results without relying on pre-stored data, thus bridging the gap between detection timing and adaptability
3Reliability
If anomaly detection computation is executed in parallel, then real-time detection is improved, but computational load increases
Solution Approach 1:
The patent applies partial action by executing anomaly detection computations with reduced computational intensity compared to full normal computations. The anomaly detection unit performs targeted comparisons and analyses only where necessary, enabling real-time detection while controlling computational load and energy consumption through selective rather than exhaustive processing
Data Source
AI summary
A computation system using a neural network model includes: processor cores; a computation allocation unit that determines a computation processor core; and an anomaly detection unit that detects an anomaly in the computation processor core. The computation allocation unit causes an anomaly detection processor core to execute an anomaly detection computation. When a difference between a first computation result of a basic computation and a second computation result of the anomaly detection computation satisfies an allowance condition, the anomaly detection unit determines that the computation processor core and the anomaly detection processor core are normal. When the difference does not satisfy the allowance condition, the anomaly detection unit determines that at least a part of the computation processor core and the anomaly detection processor core has anomaly.