论文标题
电流调制的开关电容器的低功率内像素计算
Low-power In-pixel Computing with Current-modulated Switched Capacitors
论文作者
论文摘要
我们提出了可扩展的像素内处理体系结构，该体系结构可以将数据吞吐量减少10倍，并且在Imager Frontend的每个百万像素少于30 MW。与在执行矩阵矢量乘法时调节光传感器的曝光时间（PIP）的最先进的（SOA）模拟过程不同，我们使用开关电容器和脉冲宽度调制（PWM）。这种非毁灭性方法将传感器的暴露和计算解散，从而提供了处理并行性和高数据保真度。我们的设计通过利用基于斑块的特征提取和可以执行的CNN来最大程度地减少计算复杂性和芯片密度。我们使用对所访问的对象的部分观察进一步减少数据，该对象与全帧观测紧密相关。我们一直在研究从基于变压器的后端模型进行对象分类和检测的准确性，芯片密度和功率消耗的降低。
We present a scalable in-pixel processing architecture that can reduce the data throughput by 10X and consume less than 30 mW per megapixel at the imager frontend. Unlike the state-of-the-art (SOA) analog process-in-pixel (PIP) that modulates the exposure time of photosensors when performing matrix-vector multiplications, we use switched capacitors and pulse width modulation (PWM). This non-destructive approach decouples the sensor exposure and computing, providing processing parallelism and high data fidelity. Our design minimizes the computational complexity and chip density by leveraging the patch-based feature extraction that can perform as well as the CNN. We further reduce data using partial observation of the attended objects, which performs closely to the full frame observations. We have been studying the reduction of output features as a function of accuracy, chip density and power consumption from a transformer-based backend model for object classification and detection.
