We propose an ultra-low-power (ULP) image signal processor (ISP) that performs on-the-fly in-processing frame compression/decompression and hierarchical event recognition to exploit the temporal and spatial sparsity in an image sequence. This approach reduces energy consumption spent processing and transmitting unimportant image data to achieve a 16 $\times $ imaging system energy gain in an intruder detection scenario. The ISP was fabricated in 40-nm CMOS and consumes only 170 $\mu \text{W}$ at 5 frames/s for neural network-based intruder detection and 192 $\times $ compressed image recording.

中文翻译：

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用于深层神经网络的分层图像识别的超低功耗图像信号处理器

我们提出了一种超低功耗（ULP）图像信号处理器（ISP），该处理器执行动态处理中帧压缩/解压缩和分层事件识别，以利用图像序列中的时间和空间稀疏性。这种方法减少了处理和传输不重要的图像数据以达到16 $ \次$ 入侵者检测场景中的成像系统能量增益。ISP采用40纳米CMOS制造，仅消耗170 $ \ mu \ text {W} $ 以5帧/秒的速度用于基于神经网络的入侵者检测和192 $ \次$ 压缩图像记录。