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arXiv · 2408.10233

FPCA: Field-Programmable Pixel Convolutional Array for Extreme-Edge Intelligence

Zihan Yin·Akhilesh Jaiswal
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Abstract

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.

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BibTeXRIS

Zihan Yin, Akhilesh Jaiswal. 2024-08-03. FPCA: Field-Programmable Pixel Convolutional Array for Extreme-Edge Intelligence. https://arxiv.org/abs/2408.10233

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