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Designing B-spline-based Highly Efficient Neural Networks for IoT Applications on Edge Platforms

In recent years, with the increasing demand for edge AI, the need for faster AI inference on embedded CPUs is growing. As a result, researchers have focused on various redundancies in neural networks and proposed network acceleration methods. The basic element of most neural networks is a combination of linear transformations and nonlinear activations. We believe this linearity itself is redundant and propose a new method for designing a fast neural network by using nonlinear b-spline functions instead of linear multiplications. We propose an end-to-end design flow for high-speed b-spline neural networks, which shows an actual speedup of 2.4 to 4.1 times compared to conventional neural networks when the network is implemented on Raspberry Pi Zero and Raspberry Pi Pico.