Neuromorphic photonic accelerators are becoming increasingly popular, since
they can significantly improve computation speed and energy efficiency, leading
to femtojoule per MAC efficiency. However, deploying existing DL models on such
platforms is not trivial, since a great range of photonic neural network
architectures relies on incoherent setups and power addition operational
schemes that cannot natively represent negative quantities. This results in
additional hardware complexity that increases cost and reduces energy
efficiency. To overcome this, we can train non-negative neural networks and
potentially exploit the full range of incoherent neuromorphic photonic
capabilities. However, existing approaches cannot achieve the same level of
accuracy as their regular counterparts, due to training difficulties, as also
recent evidence suggests. To this end, we introduce a methodology to obtain the
non-negative isomorphic equivalents of regular neural networks that meet
requirements of neuromorphic hardware, overcoming the aforementioned
limitations. Furthermore, we also introduce a sign-preserving optimization
approach that enables training of such isomorphic networks in a non-negative
manner.

光电神经形态计算器能够显著提高计算速度和能量效率，但将现有深度学习模型部署在这样的平台上并不容易，我们引入了一种方法来获得满足神经形态硬件要求的非负等价物，并采用保持符号优化方法来训练这样的等价网络。