Efficient deep point cloud (PC) analytics is crucial for numerous emerging applications such as autonomous vehicles and augmented and virtual reality. Our roofline model analysis reveals that the “memory wall” bottleneck primarily constrains the execution efficiency of deep PC analytics, providing valuable insight into optimization opportunities. In contrast to previous works, which greatly rely on approximating the original algorithm to fit hardware limitations, the approach presented in this paper is analytical; that is, our approach does not require any modification to the original algorithm, thus preserving its integrity and accuracy. In this paper, we introduce PointCIM, the first deep PC analytics accelerator that leverages computing-in-memory (CIM) optimization opportunities to address memory inefficiency. We identify that existing in-memory methods cannot fully support the distance function required by PC network inference. To address the challenge, we propose computation optimizations, including the Base+Offset mapping and early stopping for bit-serial computation, not only to enable full support for PC network inference in memory, but also to significantly improve hardware efficiency. We design the CIM architecture support for the proposed computation optimizations, including the memristor crossbar architecture, custom peripheral logic, data layout, and pipelined execution. Evaluation results show that the designed accelerator provides an average speedup of 17.1× and an energy reduction of 9.6× compared to the baseline of a typical edge SoC. We also compare PointCIM with several state-of-the-art PC accelerators, yielding up to 10.7× speedup and 4.9× energy savings.

A web-based telehealthcare system was integrated with the mobile application for diet people. In order to comprehensively deal with the over-weight problem, the user interface with four important aspects on weight, diet, exercise and sleep records is implemented. In addition, online courses and knowledge of diet are provided in the system. The electronic medical record of National Taiwan University Hospital is also embedded in the system for the case managers and doctors to evaluate the status of the over-weight patients. We utilized Pearson's Correlation Coefficient which represented the correlative degree of two variables to discover the relationship between the reduced weight of people and their lifestyle records. We found that three aspects of records (compliancy, violation, exercise) were important for people who wanted to lose weight.

The metabolic disorders may hinder an infant's normal physical or mental development during the neonatal period. The metabolic diseases can be treated by effective therapies if the diseases are discovered in the early stages. Therefore, newborn screening program is essential to prevent neonatal from these damages. In the paper, a support vector machine (SVM) based algorithm is introduced in place of cut-off value decision to evaluate the analyte elevation raw data associated with Phenylketonuria. The data were obtained from tandem mass spectrometry (MS/MS) for newborns. In addition, a combined feature selection mechanism is proposed to compare with the cut-off scheme. By adapting the mechanism, the number of suspected cases is reduced substantially, it also handles the medical resources effectively and efficiently.