Floating-point (FP) computing-in-memory (CIM) addresses the energy efficiency bottleneck of von Neumann architectures and fixed-point CIM in high-accuracy neural network training/inference. However, existing FP CIM designs still suffer from limited exponent-path parallelism and low mantissa-path energy efficiency. This article proposes a novel FP CIM architecture using: 1) a cascaded row-elimination comparison mechanism for single-cycle, high-parallelism exponent max-value comparison; 2) an in-memory sparse feature detection method that skips redundant mantissa computing modules based on different input/weight mantissa patterns to reduce energy consumption; and 3) separated exponent/mantissa CIM modules enabling pipelining and a mantissa bit-extension strategy optimizing accuracy, area, and energy efficiency. Simulation results of a 28-nm 19-kb macro show a 496-MHz operating frequency at 0.9 V and a peak energy efficiency of 12.88 TFLOPS/W.

Abstract Robust and fast image recognition and matching is an important task in the underwater domain. The primary focus of this work is on extracting subsea features with sonar sensor for further Autonomous Underwater Vehicle navigation, such as the robotic localization and landmark mapping applications. With the assistance of high-resolution underwater features in the Side Scan Sonar (SSS) images, an efficient feature detector and descriptor, Speeded Up Robust Feature, is employed to seabed sonar image fusion task. In order to solve the nonlinear intensity difference problem in SSS images, the main novelty of this work is the proposed Underwater Wireless Sensor Network-based Delaunay Triangulation (UWSN-DT) algorithm for improving the performances of sonar map fusion accuracy with low computational complexity, in which the wireless nodes are considered as underwater feature points, since nodes could provide sufficiently useful information for the underwater map fusion, such as the location. In the simulated experiments, it shows that the presented UWSN-DT approach works efficiently and robustly, especially for the subsea environments where there are few distinguishable feature points.