Equation 4 · Comparing the Main Approaches to Edge AI Electronics and Sensor Systems
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the fraction of time spent actively computing. Read the equation part by part below; each part has a contextual explanation and a link to its mathematical background.
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where D is the fraction of time spent actively computing and is the power drawn the rest of the time. For a keyword detector or a vibration monitor, D is typically small and falling, so is dominated by almost regardless of how fast the active phase runs — which is exactly why this category optimises the idle floor first and peak throughput second, the opposite priority from a dedicated accelerator sized for a known, recurring, higher-duty workload.
Sources cited in the article section
- [5] Eyeriss: An Energy-Efficient Reconfigurable Accelerator for Deep Convolutional Neural Networks ↗
- [11] Ethos-U55 microNPU ↗
- [17] Cadence Expands Tensilica IP Portfolio with New HiFi and Vision DSPs for Pervasive Intelligence and Edge AI Inference ↗
- [7] CMSIS-NN: Efficient Neural Network Kernels for Arm Cortex-M CPUs ↗
- [8] TensorFlow Lite Micro: Embedded Machine Learning on TinyML Systems ↗
These citations give research context. Read each source to check which claims it supports.
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