Abstract-This paper introduces a 3D-stacked logic-in-memory (LiM) system that integrates the 3D die-stacked DRAM architecture with the application-specific LiM IC to accelerate important data-intensive computing. The proposed system comprises a fine-grained rank-level 3D die-stacked DRAM device and extra LiM layers implementing logic-enhanced SRAM blocks that are dedicated to a particular application. Through silicon vias (TSVs) are used for vertical interconnections providing the required bandwidth to support the high performance LiM computing. We performed a comprehensive 3D DRAM design space exploration and exploit the efficient architectures to accelerate the computing that can balance the performance and power. Our experiments demonstrate orders of magnitude of performance and power efficiency improvements compared with the traditional multithreaded software implementation on modern CPU.

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In this paper, a novel technique based on morphological filtering is proposed for the detection and removal of ring artifacts with different patterns, e.g., sharp isolated rings with varying intensity, isolated light rings, and band rings, from the μ-and/or C-Arm CT images. To optimize the performance of our ring removal algorithm, three different types of iterative morphological filters (IMFs) are proposed here for different ring patterns. All the IMFs detect the positions of the defective detector elements using the mean curve constructed from the corrupted sinogram. However, the basic principle of sinogram correction differs. For the elimination of sharp intense rings, the IMF is directly applied on the raw projection data for each angle of view, whereas in other cases an estimate of the ring-noise-free mean curve is first obtained to remove the ring generating artifacts from the sinogram data through a normalization process. The effectiveness of the proposed IMFs is tested for a variety of μ-CT acquired images. Experimental results show that the IMFs perform better than the previously reported techniques in the removal of ring artifacts from real μ-CT images.

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