Argues that TOPS ratings and other conventional benchmarks fail to capture what actually determines edge AI performance, leaving factors such as model size, load times, and system bandwidth unmeasured. Reviews existing frameworks including the MLCommons tinyML benchmarks and proposes evaluation criteria that account for on-chip, in-memory computation alongside latency and power.

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