Expert Commentary: Deep Spiking Neural Networks and Energy Efficiency
In this article, the authors discuss the importance of energy efficiency in deep learning models and explore the potential of spiking neural networks (SNNs) as an energy-efficient alternative. SNNs are inspired by the human brain and utilize event-driven spikes for computation, offering the promise of reduced energy consumption.
The article provides an overview of the existing methods for developing deep SNNs, focusing on two main approaches: (1) ANN-to-SNN conversion, and (2) direct training with surrogate gradients. ANN-to-SNN conversion involves transforming a pre-trained artificial neural network (ANN) into an SNN, enabling the use of existing ANN architectures. Direct training with surrogate gradients, on the other hand, allows for the training of SNNs from scratch using gradient-based optimization algorithms.
Additionally, the authors categorize the network architectures for deep SNNs into deep convolutional neural networks (DCNNs) and Transformer architecture. DCNNs have shown success in computer vision tasks, while Transformer architecture has revolutionized natural language processing tasks. The exploration of these architectures in the context of SNNs opens up exciting possibilities for energy-efficient deep learning across various domains.
A significant contribution of this article is the comprehensive comparison of state-of-the-art deep SNNs, with a particular emphasis on emerging Spiking Transformers. Spiking Transformers combine the strengths of the Transformer architecture with the energy efficiency of SNNs, making them a promising avenue for future research.
Looking ahead, the authors outline future directions for building large-scale SNNs. They highlight the need for advancements in hardware design to support the efficient execution of SNN models. Additionally, they emphasize the importance of developing efficient learning algorithms that leverage the unique properties of SNNs.
Overall, this article sheds light on the potential of spiking neural networks as energy-efficient alternatives to traditional deep learning models. It provides a valuable survey of existing methods and architectures for deep SNNs and identifies the emerging trend of Spiking Transformers. The outlined future directions provide a roadmap for researchers and practitioners to further explore and develop large-scale SNNs.