Stop Moving Pixels: Mastering Zero-Copy Image Processing for High-Performance Edge AI

Short summary

The primary bottleneck in edge AI on Android is not computation but the memory wall—expensive data copies between camera, CPU, GPU, and NPU that cause frame drops and thermal throttling. Zero-copy image processing using AHardwareBuffer and dmabuf lets multiple hardware blocks share the same physical memory without copying, enabling efficient on-device AI. Google's AICore further optimizes this by centralizing LLM weights and KV cache management in a shared system process.

• •Memory data movement, not compute, is the main bottleneck in edge AI on Android
• •Zero-copy via AHardwareBuffer/dmabuf lets camera, GPU, and NPU share memory without copying
• •Google's AICore centralizes LLM weights and KV cache for multi-app efficiency on-device

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