1 research outputs found
AnalogNet: Convolutional Neural Network Inference on Analog Focal Plane Sensor Processors
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.Comment: 8 pages, 7 figure