  With the advancement in the field of the Internet of Things(IoT) and Internet
of Bodies(IoB), video camera applications using Video Sensor Nodes(VSNs) have
gained importance in the field of autonomous driving, health monitoring, robot
control, and security camera applications. However, these applications
typically involve high data rates due to the transmission of high-resolution
video signals, resulting from high data volume generated from the
analog-to-digital converters (ADCs). This significant data deluge poses
processing and storage overheads, exacerbating the problem. To address this
challenge, we propose a low-power solution aimed at reducing the power
consumption in Video Sensor Nodes (VSNs) by shifting the computation from the
digital domain to the inherently energy-efficient analog domain. Unlike
standard architectures where computation and processing are typically performed
in digital signal processing (DSP) blocks after the ADCs, our approach
eliminates the need for such blocks. Instead, we leverage a switched
capacitor-based computation unit in the analog domain, resulting in a reduction
in power consumption. We achieve a $\sim4X$ reduction in power consumption
compared to digital implementations. Furthermore, we employ a sparsity-aware
ADC, which is enabled only for significant compressed samples that contribute
to a small fraction ($\le5\%$) of the total captured analog samples, we achieve
a $\sim20X$ lower ADC conversion energy without any considerable degradation,
contributing to the overall energy savings in the system.
