TizBin: A Low-Power Image Sensor with Event and Object Detection Using Efficient Processing-in-Pixel Schemes
TizBin: A Low-Power Image Sensor with Event and Object Detection Using Efficient Processing-in-Pixel Schemes
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DOI：
10.1109/iccd56317.2022.00117
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发表时间：
2022-10
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影响因子：
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通讯作者：
Sepehr Tabrizchi;Shaahin Angizi;A. Roohi
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文献类型：
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作者：
Sepehr Tabrizchi;Shaahin Angizi;A. Roohi
在物联网人工智能（AIoT）时代，始终在线的智能自供电视觉感知系统受到广泛关注并得到广泛应用。因此，本文提出了 TizBin，这是一种具有事件和对象检测功能的低功耗处理传感器内方案，可消除数据转换和传输的功耗并支持数据密集型神经网络任务。一旦检测到移动物体，TizBin 架构就会切换到高功率物体检测模式来捕获图像。 TizBin 提供了多种独特的功能，例如模拟卷积支持低精度三元权重神经网络 (TWNN)，以减轻模拟缓冲区和模数转换器的开销。此外，TizBin 利用非易失性磁性 RAM 来存储神经网络的权重，显着降低静态功耗。我们的 TWNN 电路到应用联合仿真结果表明，在各种图像数据集上精度略有下降，而 TizBin 实现了 1000 的帧速率和 ~1.83 TOp/s/W 的效率。
In the Artificial Intelligence of Things (AIoT) era, always-on intelligent and self-powered visual perception systems have gained considerable attention and are widely used. Thus, this paper proposes TizBin, a low-power processing in-sensor scheme with event and object detection capabilities to eliminate power costs of data conversion and transmission and enable data-intensive neural network tasks. Once the moving object is detected, TizBin architecture switches to the high-power object detection mode to capture the image. TizBin offers several unique features, such as analog convolutions enabling low-precision ternary weight neural networks (TWNN) to mitigate the overhead of analog buffer and analog-to-digital converters. Moreover, TizBin exploits non-volatile magnetic RAMs to store NN’s weights, remarkably reducing static power consumption. Our circuit-to-application co-simulation results for TWNNs demonstrate minor accuracy degradation on various image datasets, while TizBin achieves a frame rate of 1000 and efficiency of ∼1.83 TOp/s/W.