论文标题
Analognet：模拟焦距传感器处理器上的卷积神经网络推断
AnalogNet: Convolutional Neural Network Inference on Analog Focal Plane Sensor Processors
论文作者
论文摘要
我们提出了一个高速，节能卷积神经网络（CNN）结构，该结构利用了一种独特的设备的功能，称为模拟焦平面传感器处理器（FPSP），其中传感器和处理器将其嵌入在同一硅芯片上。与传统视觉系统不同，传感器阵列将收集的数据发送到单独的处理器进行处理，FPSP可以在成像设备本身上处理数据。这种独特的体系结构可实现超快速的图像处理和高能效率，而牺牲了有限的处理资源和近似计算。在这项工作中，我们展示了如何将标准CNN转换为FPSP代码，并演示了一种训练网络以增加对模拟计算错误的鲁棒性的方法。我们提出的架构（创造的oragnetet）在MNIST手写数字识别任务上达到了96.9％的测试精度，速度为2260 fps，每帧成本为0.7 MJ。
We present a high-speed, energy-efficient Convolutional Neural Network (CNN) architecture utilising the capabilities of a unique class of devices known as analog Focal Plane Sensor Processors (FPSP), in which the sensor and the processor are embedded together on the same silicon chip. Unlike traditional vision systems, where the sensor array sends collected data to a separate processor for processing, FPSPs allow data to be processed on the imaging device itself. This unique architecture enables ultra-fast image processing and high energy efficiency, at the expense of limited processing resources and approximate computations. In this work, we show how to convert standard CNNs to FPSP code, and demonstrate a method of training networks to increase their robustness to analog computation errors. Our proposed architecture, coined AnalogNet, reaches a testing accuracy of 96.9% on the MNIST handwritten digits recognition task, at a speed of 2260 FPS, for a cost of 0.7 mJ per frame.
