Explain optimization mechanisms

When implementing or documenting performance optimizations, clearly explain the mechanism and expected performance benefit. For memory-bound optimizations, specify exactly how memory operations are reduced:

# Instead of:
out_mem = conv_result  # Requires writing to output memory
activation_layer(out_mem)  # Requires reading and writing again

# Better (fusion):
out_mem = activation_layer(conv_result)  # Single memory write

# For additive operations:
# Instead of:
out_mem = conv_result  # Overwrites existing data

# Better:
out_mem += conv_result  # Adds to existing data without separate read

For data-intensive operations like quantization tuning, include preparation best practices: “As input data is used many times during tuning, it is better to have it prepared earlier.” This reduces redundant processing and improves overall performance.

When explaining optimization patterns, clearly describe the benefit: “chaining operations which can be performed one after another immediately, where input of every subsequent operation is the output of the previous one” provides specific insight into why the optimization helps. Precise explanations help developers implement optimizations correctly and make informed decisions about performance trade-offs.