The Impact of Feature Representation on the Accuracy of Photonic Neural Networks
CoRR(2024)
Abstract
Photonic Neural Networks (PNNs) are gaining significant interest in the
research community due to their potential for high parallelization, low
latency, and energy efficiency. PNNs compute using light, which leads to
several differences in implementation when compared to electronics, such as the
need to represent input features in the photonic domain before feeding them
into the network. In this encoding process, it is common to combine multiple
features into a single input to reduce the number of inputs and associated
devices, leading to smaller and more energy-efficient PNNs. Although this
alters the network's handling of input data, its impact on PNNs remains
understudied. This paper addresses this open question, investigating the effect
of commonly used encoding strategies that combine features on the performance
and learning capabilities of PNNs. Here, using the concept of feature
importance, we develop a mathematical methodology for analyzing feature
combination. Through this methodology, we demonstrate that encoding multiple
features together in a single input determines their relative importance, thus
limiting the network's ability to learn from the data. Given some prior
knowledge of the data, however, this can also be leveraged for higher accuracy.
By selecting an optimal encoding method, we achieve up to a 12.3
in accuracy of PNNs trained on the Iris dataset compared to other encoding
techniques, surpassing the performance of networks where features are not
combined. These findings highlight the importance of carefully choosing the
encoding to the accuracy and decision-making strategies of PNNs, particularly
in size or power constrained applications.
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