  Point cloud analytics is poised to become a key workload on battery-powered
embedded and mobile platforms in a wide range of emerging application domains,
such as autonomous driving, robotics, and augmented reality, where efficiency
is paramount. This paper proposes Mesorasi, an algorithm-architecture
co-designed system that simultaneously improves the performance and energy
efficiency of point cloud analytics while retaining its accuracy. Our extensive
characterizations of state-of-the-art point cloud algorithms show that, while
structurally reminiscent of convolutional neural networks (CNNs), point cloud
algorithms exhibit inherent compute and memory inefficiencies due to the unique
characteristics of point cloud data. We propose delayed-aggregation, a new
algorithmic primitive for building efficient point cloud algorithms.
Delayed-aggregation hides the performance bottlenecks and reduces the compute
and memory redundancies by exploiting the approximately distributive property
of key operations in point cloud algorithms. Delayed-aggregation let point
cloud algorithms achieve 1.6x speedup and 51.1% energy reduction on a mobile
GPU while retaining the accuracy (-0.9% loss to 1.2% gains). To maximize the
algorithmic benefits, we propose minor extensions to contemporary CNN
accelerators, which can be integrated into a mobile Systems-on-a-Chip (SoC)
without modifying other SoC components. With additional hardware support,
Mesorasi achieves up to 3.6x speedup.
