Convolutional Spiking Neural Networks (SNN) offer the potential for highly energy-efficient vision processing by exploiting sparse, event-driven computation. However, existing SNN accelerators underutilize the inherent parallelism of convolutional layers and lack the flexibility to accommodate varying memory demands and input sparsity across layers. This paper presents Mega, a digital architecture for convolutional SNNs that addresses these limitations through three key contributions: (1) highly parallel acceleration of $3 \times 3$ convolutions, (2) a unified data memory for spikes, neuron states, and weights, and (3) efficient spike map processing with low-overhead spike detection. Fabricated in GlobalFoundries 22 nm FDSOI technology, Mega achieves an energy efficiency of 0.375 pJ/SOP, improving the state of the art by $4\times$.

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