Performance estimation for the memristor-based computing-in ...
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This study showcases an algorithm-hardware co-design to realize real-time and low-power semantic segmentation at the edge.
It is shown by the simulation in DNN+NeuroSim V2.0 that the memristor-based CIM accelerator is ∼63× (∼4.6×) smaller in area, at most ∼9.2× (∼1000×) faster, and ...
Sep 23, 2024 · This study showcases an algorithm-hardware co-design to realize real-time and low-power semantic segmentation at the edge. ResearchGate Logo.
Mar 1, 2023 · Here, we first propose an extremely factorized network (EFNet) which can learn multi-scale context information while preserving rich spatial ...
Performance estimation for the memristor-based computing-in-memory implementation of extremely factorized network for real-time and low-power semantic ...
... estimation for the memristor-based computing-in-memory implementation of extremely factorized network for real-time and low-power semantic segmentation.
This paper focuses on building a network with better performance possible while still achieve real-time inference speed, and uses a pyramid kernel size to ...
S Dong, Performance estimation for the memristor-based computing-in-memory implementation of extremely factorized network for real-time and low-power ...
Feb 19, 2024 · Memristive devices have gained significant attention as key components for in-memory computing due to their high-density arrays, rapid response ...
Missing: estimation extremely factorized semantic
Sep 28, 2023 · In memristor-based image processing networks, the fast-switching speed, and low power consumption of memristors directly reduce the image ...
Missing: semantic | Show results with:semantic