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OASIS, a distributed in-sensor vision framework that uses a lightweight encoder to generate compact, task-relevant representations before off-chip transmission, is presented, demonstrating a practical hardware-algorithm co-design path for communication-efficient in-sensor vision.
This work proposes FALCON, a fault-tolerant, MTJ-based in-memory arithmetic architecture integrated with Stochastic Computing (SC), and investigates the proper functionality of FALCON on morphological closing as a realistic noise-tolerant image processing case study.
This work introduces MITRA, a reconfigurable magnetic tunnel junction-based in-memory architecture that leverages stochastic computing (SC) to implement a broad class of transcendental and nonlinear functions directly within memory.
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