Towards In-Situ Neuromorphic Computing Architecture for Event Stream Super-Resolution

Event-based cameras, with their unique event stream representation, effectively mitigate motion blur in highspeed, high-exposure scenarios but suffer from low spatial resolution. To address this, we propose a super-resolution hardware accelerator for event streams based on Spiking Neural Networks (SNNs). In terms of network architecture, we incorporate hardware-friendly algorithmic designs by simplifying neuron models and optimizing convolution operations. On the hardware side, the design adopts a hierarchical structure featuring a highly parallel computational array. Additionally, by proposing a Kernel-Channel-Timestamp-Row (KCTR) dataflow and dual-pipeline structure, the design achieves in-situ computing, eliminating intermediate storage within layers and significantly reducing inter-layer spike storage. Evaluations on the N-MNIST and ASL-DVS datasets demonstrate root mean square errors (RMSE) of $\mathbf{1. 2 9 6}$ and $\mathbf{0. 1 2 1}$ for reconstructed super-resolution event streams. In downstream applications, the classification accuracies reach 98.84% and 99.73%, respectively. The proposed accelerator, designed using a 28 nm CMOS process, improves reconstruction speed by 95.6% compared to a GPU, operates at 500 MHz, and consumes only 0.546 pJ per synaptic operation.