  Due to the high activation sparsity and use of accumulates (AC) instead of
expensive multiply-and-accumulates (MAC), neuromorphic spiking neural networks
(SNNs) have emerged as a promising low-power alternative to traditional DNNs
for several computer vision (CV) applications. However, most existing SNNs
require multiple time steps for acceptable inference accuracy, hindering
real-time deployment and increasing spiking activity and, consequently, energy
consumption. Recent works proposed direct encoding that directly feeds the
analog pixel values in the first layer of the SNN in order to significantly
reduce the number of time steps. Although the overhead for the first layer MACs
with direct encoding is negligible for deep SNNs and the CV processing is
efficient using SNNs, the data transfer between the image sensors and the
downstream processing costs significant bandwidth and may dominate the total
energy. To mitigate this concern, we propose an in-sensor computing
hardware-software co-design framework for SNNs targeting image recognition
tasks. Our approach reduces the bandwidth between sensing and processing by
12-96x and the resulting total energy by 2.32x compared to traditional CV
processing, with a 3.8% reduction in accuracy on ImageNet.
