In genome analysis, it is often important to identify variants from a reference genome. However, identifying variants that occur with low frequency can be challenging, as it is computationally intensive to do so accurately. LoFreq is a widely used program that is adept at identifying low-frequency variants. This article presents a design framework for an FPGA-based accelerator for LoFreq. In particular, this accelerator is targeted at virus analysis, which is particularly challenging, compared to human genome analysis, as the characteristics of the data to be analyzed are fundamentally different. Across the design space, this accelerator can achieve up to 120× speedups on the core computation of LoFreq and speedups of up to 51.7× across the entire program.

In genome analysis, it is often important to identify variants from a reference genome. However, identifying variants that occur with low frequency can be challenging, as it is computationally intensive to do so accurately. LoFreq is a widely used program that is adept at identifying low frequency variants. This paper presents an FPGA-based accelerator for LoFreq. In particular, this accelerator is targeted at virus analysis, which is particularly challenging, compared to human genome analysis, as the characteristics of the data to be analyzed are fundamentally different. This accelerator can achieve up to 120× speedups on the core computation of LoFreq and speedups of up to 32.4× across the entire program.

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 1.6× 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 3.6× speedup.

Machine perception applications are increasingly moving toward manipulating and processing 3D point cloud. This paper focuses on point cloud registration, a key primitive of 3D data processing widely used in high-level tasks such as odometry, simultaneous localization and mapping, and 3D reconstruction. As these applications are routinely deployed in energy-constrained environments, real-time and energy-efficient point cloud registration is critical.