Abstract: 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.
We are at a transitional moment in how disease control efforts are structured in middle-income countries (MICs). To meet new evolving opportunities and threats in the coming decade in MICs, we cannot ...
Abstract: the paper proposes moving from spatial problems to problems on the plane in similar problems. This will significantly reduce the machine memory requirements and shorten the calculation time.
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