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To summarize, we propose an efficient dynamic neural network for multispectral detection. Compared with the unoptimized static model, our proposed model reduces computation by 41% and off-chip memory access by 77% on.
We design a cross-layer scheduling for dynamic neural networks to improve the computation to communication (CTC) ratio. We designed the scheduling arbitration module, which can fine-tune fusion strategy according to the activation ratio of each layer at runtime to obtain the optimal scheduling scheme.
We implement a flexible FPGA-based hardware accelerator to evaluate our design. Experiments demonstrate that our dynamic neural network accelerator achieves a maximum 2.7Ąż inference speedup.
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IEEE/ICACT20230038 Slide.15
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