add note on low level intrinsics

programming_guide/section-4/section-4c/README.md

@@ -113,6 +113,8 @@ void scale_vectorized(T *a, T *c, int32_t factor, const int32_t N) {
 
 In this first example, the vectorization strategy was relatively straight forward. Instead of iterating over a vector of values and doing a single scalar multiply, we load a vector of input values, iterate over a smaller loop to perform a vector*scalar operation using the AIE API functions, and then store the vector of results back to local memory.
 
+> **NOTE** - AIE API is a portable programming interface that is implemented as a C++ header-only library providing types and operations that get translated into  generation specific efficient low-level intrinsics. AIE kernels can also be programmed directly in these low-level C++ intrinsics: [AIE1 Intrinsics User Guide - v2023.2](https://www.xilinx.com/htmldocs/xilinx2023_2/aiengine_intrinsics/intrinsics/index.html) and [AIE2 Intrinsics User Guide - v2023.2](https://www.xilinx.com/htmldocs/xilinx2023_2/aiengine_ml_intrinsics/intrinsics/index.html)
+
 ## <u>Vectorization Exercises</u>
 1. Let's take a look at the trace for our vector scalar design. First, let's edit our [vector_scalar_mul design](../../../programming_examples/basic/vector_scalar_mul/) so that the [aie2.py](../../../programming_examples/basic/vector_scalar_mul/aie2.py) source file has `vectorized=False`. In the [aie2.py](../../../programming_examples/basic/vector_scalar_mul/aie2.py) sourcee code, we simply select the scalar version of the kernel function. Then run `make trace`. After the trace compilation is complete, open `trace_vs.json` in https://ui.perfetto.dev and measure the delta between `event 0` and `event 1`. Note that in the Perfetto waveform, 1 us is equal to 1 clock cycle. How many cycles did you measure? <img src="../../../mlir_tutorials/images/answer1.jpg" title="~10,256 cycles" height=25>