FPCA: Field-Programmable Pixel Convolutional Array for Extreme-Edge Intelligence
<|reference_start|>FPCA: Field-Programmable Pixel Convolutional Array for Extreme-Edge Intelligence: The rapid advancement of neural network applications necessitates hardware that not only accelerates computation but also adapts efficiently to dynamic processing requirements. While processing-in-pixel has emerged as a promising solution to overcome the bottlenecks of traditional architectures at the extreme-edge, existing implementations face limitations in reconfigurability and scalability due to their static nature and inefficient area usage. Addressing these challenges, we present a novel architecture that significantly enhances the capabilities of processing-in-pixel for convolutional neural networks. Our design innovatively integrates non-volatile memory (NVM) with novel unit pixel circuit design, enabling dynamic reconfiguration of synaptic weights, kernel size, channel size and stride size. Thus offering unprecedented flexibility and adaptability. With using a separate die for pixel circuit and storing synaptic weights, our circuit achieves a substantial reduction in the required area per pixel thereby increasing the density and scalability of the pixel array. Simulation results demonstrate dot product operations of the circuit, the non-linearity of its analog output and a novel bucket-select curvefit model is proposed to capture it. This work not only addresses the limitations of current in-pixel computing approaches but also opens new avenues for developing more efficient, flexible, and scalable neural network hardware, paving the way for advanced AI applications.<|reference_end|>
@article{yin2024fpca:,
    title={FPCA: Field-Programmable Pixel Convolutional Array for Extreme-Edge
  Intelligence},
    author={Zihan Yin, Akhilesh Jaiswal},
    journal={arXiv preprint arXiv:2408.10233},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.10233},
    primaryClass={cs.AR eess.IV}
}