HOGEye: Neural Approximation of HOG Feature Extraction in RRAM-Based 3D-Stacked Image Sensors
HOGEye: Neural Approximation of HOG Feature Extraction in RRAM-Based 3D-Stacked Image Sensors
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DOI：
10.1145/3531437.3539706
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发表时间：
2022-08
期刊：
影响因子：
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通讯作者：
T. Ma;Weidong Cao;Fei Qiao;Ayan Chakrabarti;Xuan Zhang
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作者：
T. Ma;Weidong Cao;Fei Qiao;Ayan Chakrabarti;Xuan Zhang
许多计算机视觉任务，从识别到多视图配准，都是对图像的特征表示而不是原始像素强度进行操作。然而，用于获得这些表示的常规流水线由于逐像素模数（A/D）转换以及昂贵的存储和计算而导致显著的能量消耗。在本文中，我们提出了HOGEye，一个有效的近像素实现的一个广泛使用的特征提取算法-直方图的方向一致性（HOG）。HOGEye通过在基于电阻式随机存取存储器（RRAM）的3D堆叠图像传感器中应用新型神经近似方法，将关键但计算密集的导数提取（DE）和直方图生成（HG）步骤移动到模拟域。感知（传感器）和计算（DE和HG）的协同定位以及A/D转换的缓解使HOGEye设计能够实现显著的节能。在检测速率下降可以忽略不计的情况下，整个HOGEye传感器系统在30 fps下的功耗低于48μ W，图像分辨率为256 × 256（相当于24.3pJ/pixel），而处理部分的功耗仅为14.1pJ/pixel，实现了比最先进设计高2.5倍的能效提升。
Many computer vision tasks, ranging from recognition to multi-view registration, operate on feature representation of images rather than raw pixel intensities. However, conventional pipelines for obtaining these representations incur significant energy consumption due to pixel-wise analog-to-digital (A/D) conversions and costly storage and computations. In this paper, we propose HOGEye, an efficient near-pixel implementation for a widely-used feature extraction algorithm—Histograms of Oriented Gradients (HOG). HOGEye moves the key but computation-intensive derivative extraction (DE) and histogram generation (HG) steps into the analog domain by applying a novel neural approximation method in a resistive random-access memory (RRAM)-based 3D-stacked image sensor. The co-location of perception (sensor) and computation (DE and HG) and the alleviation of A/D conversions allow HOGEye design to achieve significant energy saving. With negligible detection rate degradation, the entire HOGEye sensor system consumes less than 48μW@30fps for an image resolution of 256 × 256 (equivalent to 24.3pJ/pixel) while the processing part only consumes 14.1pJ/pixel, achieving more than 2.5 × energy efficiency improvement than the state-of-the-art designs.