Enhanced Computational Storage Device Employing AI-based Triage

The popularity of Artificial Intelligence (AI) applications is counterbalanced by their cost in terms of time and energy. Traditional computing systems have evolved with separate computing and storage units, which require data movement in order to perform data processing. Computational Storage Device (CSD) technologies have been proposed in order to push data processing to the data in order to reduce time consumption, memory, energy, and bandwidth usage. In this paper, an Enhanced CSD (3CSD) will be introduced. An efficient and flexible storage device-based triage, that provides a seamless workflow to process and infer data in place represents an evolution in terms of traditional CSD which employs a store-first inference-later paradigm. The experimental results show that the purpose of Multistage LILLIAN (LeveragIng Last-miLe data usIng context-based AutolabelliNg) increases the performance of the triage detectors by 20%. Furthermore, some state-of-the-art fusion approaches are evaluated including late fusion (Ensembles) which boosted the performance of the triage subsystem significantly. Experimental results are evaluated using Intel Movidius MyriadX.