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- W4360605972 abstract "Cost-volume construction, which accurately computes the similarities between pixels in paired images, is a fundamental kernel of stereo vision processing and has been directly used in robotic, autopilot, and AR/VR applications. However, the large parameter size and consecutive data accesses of real-time cost-volume construction (>30fps) exerts a high demand on the memory bandwidth (0.254Tb/s) and operation (391GOPs). A promising candidate to resolve the memory bottleneck is computation-in-memory (CIM), which provides computing parallelism based on a structured array [1–7]. However, to deploy CIM applications one needs to overcome three challenges: 1) Considering various image properties and frame rate scenarios, cost functions are pattern-dependent and could be, in essence, classified as distances between pixels. CIM only supports restrictedvector Boolean logic, but distance operations require combining variable pixel/vector arithmetic functions. 2) Cost functions are sensitive to the disturbance caused by the reduced CIM computing margin, which is even worse with higher output. Furthermore, distance computing cannot take advantage of input sparsity to reduce the output. 3) The sliding search plane alternates activating weights and incurs data loading overheads, which mismatches the rigid structured array and leads to low utilization." @default.
- W4360605972 created "2023-03-24" @default.
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- W4360605972 date "2023-02-19" @default.
- W4360605972 modified "2023-10-17" @default.
- W4360605972 title "7.7 CV-CIM: A 28nm XOR-Derived Similarity-Aware Computation-in-Memory for Cost-Volume Construction" @default.
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- W4360605972 doi "https://doi.org/10.1109/isscc42615.2023.10067720" @default.
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