đī¸ 8xA55 ARM CPU Perf
When porting drone-related algorithms such as path planning (FastPlanner, EgoPlanner), visual input/output (VINS-Fusion), and lidar input/output (Fast-LIO) to the 8-core A55 ARM CPU, targeted optimization is required based on the ARM architecture's characteristics (such as multi-core and NEON SIMD) and the algorithm's computationally intensive features (matrix operations, nonlinear optimization, and point cloud processing). The following is a detailed optimization approach, organized according to the logic of algorithm characteristics â architecture adaptation â engineering practice:
đī¸ ARM Perf
Improving the performance of edge computing ARM chips requires a combination of hardware features, software optimization, and system configuration. The following is a classification of specific methods:
đī¸ Arm Performance Libraries
When replacing Eigen's backend with the Arm Performance Libraries (APL), which includes optimized linear algebra implementations such as Arm BLAS and LAPACK, the core idea is to have Eigen call the underlying linear algebra functions (such as matrix multiplication and decomposition) provided by APL, which are optimized for the Arm architecture, rather than Eigen's own general-purpose implementations. This allows APL to leverage the instruction set (such as NEON) and architectural features optimized for Arm CPUs (such as the A55) to improve computing performance. The following are the specific steps and precautions:
đī¸ EMLL
On-device