  Machine Learning compilers like TVM allow a fast and flexible deployment on
embedded CPUs. This enables the use of non-standard operators, which are common
in ML compression techniques. However, it is necessary to understand the
limitations of typical compute-intense operators in ML workloads to design a
proper solution. This is the first in-detail analysis of dense and convolution
operators, generated with TVM, that compares to the fundamental hardware limits
of embedded ARM processors. Thereby it explains the gap between computational
peak performance, theoretical and measured, and real-world state-of-the-art
results, created with TVM and openBLAS. Instead, one can see that
single-precision general matrix multiply (GEMM) and convolutions are bound by
L1-cache-read bandwidth. Explorations of 8-bit and bit-serial quantized
operators show that quantization can be used to achieve relevant speedups
compared to cache-bound floating-point operators. However, the performance of
quantized operators highly depends on the interaction between data layout and
bit packing.
