To achieve high AI Engine performance, focus on maximizing vector processor utilization. Vectorize the algorithm and manage vector registers, memory access, and software pipelining to keep data loading while computation executes. Because the vector processor issues one operation per cycle, schedule data for the next operation during the current cycle. Start vectorization by selecting data types and vector intrinsics that operate on multiple elements in parallel. If the inner loop has dependencies, unroll an outer loop to compute multiple values concurrently. Choose AI Engine kernels for workloads that benefit from vector processing and offload other work to the Processing System, Programmable Logic, or DSP engines when appropriate.
To support single‑kernel AI Engine development, the Vitis Unified IDE provides dedicated AI Engine kernel development support. The IDE includes a single‑node graph example for rapid prototyping. The Debug view shows registers, variables, breakpoints, register and memory mappings, memory contents, and the instruction pipeline for each kernel.
--profile option.