Global IP-video traffic is projected to exceed 4.8 Zettabytes annually by 2025, driven largely by edge applications like autonomous driving and urban mobility that generate hundreds of terabytes per device. While significant research has optimized edge inference and training architectures, the critical challenges of data archival and storage have been largely overlooked. Our analysis reveals that archival operations, not inference, dominate system resources, consuming up to 80% of memory bandwidth and one-third of CPU cycles in edge servers. We present Salient Store, a novel computational storage architecture that transforms passive storage devices into active participants in the video analytics pipeline. Salient Store integrates motion-aware layered neural compression directly within storage FPGAs, reusing feature maps from inference to eliminate redundant computation while employing anchor-delta temporal encoding to maximize compression efficiency. By executing these operations entirely within the storage plane through peer-to-peer communication, our system bypasses host memory bottlenecks that plague traditional architectures. Comprehensive evaluation across five real-world datasets demonstrates that Salient Store reduces end-to-end archival latency by $6.18 \times$, decreases host-side data movement by $5.63 \times$, and maintains up to 47 dB PSNR while reducing system power consumption by 65%. This work fundamentally re-imagines storage for continuous learning systems, transforming it from a passive bottleneck into an acceleration layer that operates symbiotically with neural inference and training.

The deployment of Deep Neural Networks (DNNs) in energy-constrained environments, such as Energy Harvesting Wireless Sensor Networks (EH-WSNs), introduces significant challenges due to the intermittent nature of power availability. This study introduces NExUME, a novel training methodology designed specifically for DNNs operating under such constraints. We propose a dynamic adjustment of training parameters—dropout rates and quantization levels—that adapt in real-time to the available energy, which varies in energy harvesting scenarios. This approach utilizes a model that integrates the characteristics of the network architecture and the specific energy harvesting profile. It dynamically adjusts training strategies, such as the intensity and timing of dropout and quantization, based on predictions of energy availability. This method not only conserves energy but also enhances the network’s adaptability, ensuring robust learning and inference capabilities even under stringent power constraints. Our results show a 6% to 22% improvement in accuracy over current methods, with an increase of less than 5% in computational overhead. This paper details the development of the adaptive training framework, describes the integration of energy profiles with dropout and quantization adjustments, and presents a comprehensive evaluation using real-world data. Additionally, we introduce a novel dataset aimed at furthering the application of energy harvesting in computational settings.