The content delves into the performance analysis of matrix-matrix multiplication (gemm) kernels on processors designed for edge computing. It discusses the challenges posed by heterogeneous IoT architectures and the need for careful optimization of software running on these devices. The study focuses on gemm as a crucial kernel for deep neural networks in various applications like signal processing and natural language processing. The authors contribute by using a simulator based on GotoBLAS2 and BLIS frameworks to experiment with different algorithmic alternatives before implementation. They calibrate the simulator with experimental data to provide accurate estimations of execution time for specific processor architectures, such as GAP8 parallel-ultra-low power processor.
The paper also discusses blocked algorithms for gemm, detailing baseline algorithms and alternative variants that optimize memory hierarchy usage. It highlights the importance of micro-kernel dimensions in achieving efficient gemm performance tailored to specific IoT architectures. The study validates the performance simulator using a GAP8 PULP platform and demonstrates its accuracy in estimating execution times compared to actual implementations.
Furthermore, it evaluates three algorithmic variants for gemm, comparing their performance based on micro-kernel dimensions and layer characteristics from MobileNetV1 DNN. The results show significant variability in execution times across different layers and algorithmic variants, emphasizing the need for architecture-specific optimizations.
Overall, the content provides valuable insights into optimizing gemm performance for IoT processors through simulation-based analysis and highlights future research directions to enhance modeling complexities associated with cache memories and DMA controllers.
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