IIT Bhilai Researcher Cuts Anomaly Detection Runtime 12x Using GPU Acceleration

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An M.Tech student at IIT Bhilai developed a GPU-accelerated pipeline for detecting anomalies in multivariate industrial time-series data as part of his 2021–2023 thesis. The research built on the MSCRED architecture, which uses convolutional and ConvLSTM layers to model relationships between multiple sensor signals across different time windows. The convolution-heavy encoder created a major CPU bottleneck, prompting the implementation of CUDA-based optimisations using im2col and GEMM workflows. By integrating these custom operations with PyTorch, encoder runtime dropped from 300 seconds on CPU to roughly 23.7 seconds on GPU — a speedup of approximately 12.68 times. The work demonstrates how low-level GPU programming techniques can significantly accelerate deep learning pipelines used in industrial anomaly detection.

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