JOVS: Joint Optimization of Vectorization and Scheduling for DNN on AI DSPs

Recent embedded devices have integrated digital signal processors (DSPs) to balance performance and power when executing complex Deep Neural Network (DNN) workloads. With modern AI DSPs providing specialized tensor computation vector instructions and limited on-chip memory, fully releasing the potential of these DSPs remains a significant challenge. The performance of AI DSPs relies heavily on vendor-provided libraries and compilers. In practice, vendor-provided libraries are inflexible and prevent further optimization. State-of-the-art compilers usually focus on a single optimization (vectorization or scheduling), which is insufficient to address this challenge.