Non-invasive surface registration methods have been developed to register and track breathing motions in a patient’s abdomen and thorax. We evaluated several different registration methods, including marker tracking using a stereo camera, chessboard image projection, and abdominal point clouds. Our point cloud approach was based on a time-of-flight (ToF) sensor that tracked the abdominal surface. We tested different respiratory phases using additional markers as landmarks for the extension of the non-rigid Iterative Closest Point (ICP) algorithm to improve the matching of irregular meshes. Four variants for retrieving the correspondence data were implemented and compared. Our evaluation involved 9 healthy individuals (3 females and 6 males) with point clouds captured in opposite breathing phases (i.e., inhalation and exhalation). We measured three factors: surface distance, correspondence distance, and marker error. To evaluate different methods for computing the correspondence measurements, we defined the number of correspondences for every target point and the average correspondence assignment error of the points nearest the markers.

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Simple graph algorithms such as PageRank have recently been the target of numerous hardware accelerators. Yet, there also exist much more complex graph mining algorithms for problems such as clustering or maximal clique listing. These algorithms are memory-bound and thus could be accelerated by hardware techniques such as Processing-in-Memory (PIM). However, they also come with non-straightforward parallelism and complicated memory access patterns. In this work, we address this with a simple yet surprisingly powerful observation: operations on sets of vertices, such as intersection or union, form a large part of many complex graph mining algorithms, and can offer rich and simple parallelism at multiple levels. This observation drives our cross-layer design, in which we (1) expose set operations using a novel programming paradigm, (2) express and execute these operations efficiently with carefully designed set-centric ISA extensions called SISA, and (3) use PIM to accelerate SISA instructions. The key design idea is to alleviate the bandwidth needs of SISA instructions by mapping set operations to two types of PIM: in-DRAM bulk bitwise computing for bitvectors representing high-degree vertices, and near-memory logic layers for integer arrays representing low-degree vertices. Set-centric SISA-enhanced algorithms are efficient and outperform hand-tuned baselines, offering more than 10× speedup over the established Bron-Kerbosch algorithm for listing maximal cliques. We deliver more than 10 SISA set-centric algorithm formulations, illustrating SISA's wide applicability.

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