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- W4376273409 abstract "Spiking Neural Networks (SNNs) show great potential for solving Artificial Intelligence (AI) applications. At the preliminary stage of SNNs, benchmarks are essential for evaluating and optimizing SNN algorithms, software, and hardware toward AI scenarios. However, a majority of SNN benchmarks focus on evaluating SNN for brain science, which has distinct neural network architectures and targets. Even though there have several benchmarks evaluating SNN for AI, they only focus on a single stage of training and inference or a processing fragment of a whole stage without accuracy information. Thus, the existing SNN benchmarks lack an end-to-end perspective that not only covers both training and inference but also provides a whole training process to a target accuracy level. This paper presents SNNBench—the first end-to-end AI-oriented SNN benchmark covering the processing stages of training and inference and containing the accuracy information. Focusing on two typical AI applications: image classification and speech recognition, we provide nine workloads that consider the typical characteristics of SNN, i.e., the dynamics of spiking neurons, and AI, i.e., learning paradigms including supervised and unsupervised learning, learning rules like backpropagation, connection types like fully connected, and accuracy. The evaluations of SNNBench on both CPU and GPU show its effectiveness. The specifications, source code, and results will be publicly available from https://www.benchcouncil.org/SNNBench." @default.
- W4376273409 created "2023-05-13" @default.
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- W4376273409 date "2023-02-01" @default.
- W4376273409 modified "2023-09-25" @default.
- W4376273409 title "SNNBench: End-to-end AI-oriented spiking neural network benchmarking" @default.
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- W4376273409 doi "https://doi.org/10.1016/j.tbench.2023.100108" @default.
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