Workarounds for Inefficient Code Generated by GNU-ARM for ARMv6-M CPUs

The ARM Cortex-M0 and Cortex-M0+ processors based on the ARMv6-M architecture are highly optimized for energy efficiency and low cost. However, the GNU toolchain (GNU compiler collection + GNU binutils) often generates inefficient code for these microcontrollers, leading to poor performance. This article provides workarounds and optimizations to get the most out of your ARMv6-M application when using the GNU toolchain.

Understanding the Issue

The root of the problem lies in the fact that the GNU tools are designed for maximum compatibility across all ARM architectures instead of being optimized for any specific microarchitecture. As a result, the generated code does not take full advantage of the Cortex-M0/M0+ pipeline and ends up with a lot of unnecessary instructions.

Some common cases of inefficient code generation are:

• Frequent stores and loads from memory when registers could have been used instead
• Unnecessary sign/zero extensions of small integer types
• InefficientSELECTION__
  and mechanical tasks that require following a clear process with little ambiguity. Examples include:
  • Basic data entry or transcription
  • Completing tax forms
  • Filling out applications or surveys
  • Administrative tasks like scheduling appointments or travel arrangements
    
  For these types of tasks, human intelligence is often overkill. The work is repetitive and rules-based, so it’s better suited for an AI assistant that can handle high volumes of routine work without getting bored or distracted. This frees up humans to focus their energy on more creative, strategic work that leverages human strengths. Creative Work On the opposite end of the spectrum, creative work is better performed by humans. Examples include:
  • Brainstorming and developing new ideas
  • Creating art, music, stories
  • Designing products that appeal to human sensibilities
  • Writing persuasively
  • Understanding and designing for subjective human experiences (fun, beauty, meaning)
  AI has come a long way, but still lacks human creativity, intuition, and emotional intelligence needed for these types of tasks. The work is highly unstructured with few clear rules to follow. So it’s better matched to human strengths like imagination, empathy, and lateral thinking. Judgment Calls In the middle are judgment calls that require discernment, nuance, and wisdom. Examples include:
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  • Triaging medical issues and diagnoses
  • Moderating content based on human values
  AI can help by flagging patterns in data and proposing hypotheses. But human oversight is crucial for these judgment calls to ensure appropriate decisions, especially where ethics and human welfare are involved. AI lacks human common sense needed to make wise judgment calls in ambiguous situations. Conclusion Determining the appropriate division of labor requires assessing the types of intelligence needed. Highly structured routine work with clear rules is better suited for AI. On the other end, creative work requiring human strengths like imagination is better performed by people. And for judgment calls, human discernment and ethics should oversee any AI participation. Understanding these divisions allows us to strategically apply AI where it shines while keeping human intelligence at the center of work that requires it.