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Deep Learning is developing rapidly and has made breakthroughs in various fields. As a result, the number of parameters in deep neural networks (DNNs) is scaling about 5× rate of Moore’s Law. To meet the computing power needs of DNNs, new computing architectures have been proposed, and the electro-photonic computing system is one of them. With the physical characteristics of photons, the electro-photonic computing system has demonstrated great potential in improving the efficiency of convolution calculation. However, there are still much space for improvement in the energy efficiency of the electro-photonic computing system. And the high energy consumption led by the high-precision analog-to-digital converters (ADCs) is an important factor hindering the energy efficiency. ADCs dominate about 50% energy consumption of the entire system, and they will increase exponentially with the precision. But if the low-precision ADCs are used directly, they will destroy the computing accuracy in the convolution calculation and reduce the inference performance of DNNs. In this paper, we propose an energy-efficient quantized inference framework for electro-photonic computing system to balance the energy consumption of ADCs and the DNNs inference performance, which includes a multi-scale quantization scheme based on per-slice granularity and width variable optical matrix. The proposed framework can reduce the energy consumption of the system by using low-precision ADCs, and ensure the inference performance of the DNNs at the same time. The experiments are carried out in MobileNet-v2, ResNet50 and Vgg16 respectively. The experimental results show that the proposed quantized inference framework can reduce the precision of ADCs by 7 bits and compared with using the low-precision ADCs directly, it improves the model accuracy of 40.5%, 70.2% and 65.4% respectively, which is close to the full precision model.
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