Spiking Neural Networks (SNNs) offer a promising solution to achieve ultra
low-power/energy computation for solving machine learning tasks. Currently,
most of the SNN architectures are derived from Artificial Neural Networks whose
neurons' architectures and operations are different from SNNs, or developed
without considering memory budgets from the underlying processing hardware.
These limitations hinder the SNNs from reaching their full potential in
accuracy and efficiency. Towards this, we propose SpikeNAS, a novel
memory-aware neural architecture search (NAS) framework for SNNs that can
quickly find an appropriate SNN architecture with high accuracy under the given
memory budgets. To do this, our SpikeNAS employs several key steps: analyzing
the impacts of network operations on the accuracy, enhancing the network
architecture to improve the learning quality, and developing a fast
memory-aware search algorithm. The experimental results show that our SpikeNAS
improves the searching time and maintains high accuracy as compared to
state-of-the-art while meeting the given memory budgets (e.g., 4.4x faster
search with 1.3% accuracy improvement for CIFAR100, using an Nvidia RTX 6000
Ada GPU machine), thereby quickly providing the appropriate SNN architecture
for memory-constrained SNN-based systems.

SpikeNAS 是一种新颖的内存感知神经架构搜索（NAS）框架，用于 SNN，能够在给定的内存预算下快速找到适当的 SNN 架构，提高准确性并满足内存受限的 SNN 系统的需求。

SpikeNAS：一种用于脉冲神经网络系统的快速、内存感知的神经架构搜索框架

SpikeNAS: A Fast Memory-Aware Neural Architecture Search Framework for  Spiking Neural Network Systems

As spiking neural networks receive more attention, we look toward
applications of this computing paradigm in fields other than computer vision
and signal processing. One major field, underexplored in the neuromorphic
setting, is Natural Language Processing (NLP), where most state-of-the-art
solutions still heavily rely on resource-consuming and power-hungry traditional
deep learning architectures. Therefore, it is compelling to design NLP models
for neuromorphic architectures due to their low energy requirements, with the
additional benefit of a more human-brain-like operating model for processing
information. However, one of the biggest issues with bringing NLP to the
neuromorphic setting is in properly encoding text into a spike train so that it
can be seamlessly handled by both current and future SNN architectures. In this
paper, we compare various methods of encoding text as spikes and assess each
method's performance in an associated SNN on a downstream NLP task, namely,
sentiment analysis. Furthermore, we go on to propose a new method of encoding
text as spikes that outperforms a widely-used rate-coding technique, Poisson
rate-coding, by around 13\% on our benchmark NLP tasks. Subsequently, we
demonstrate the energy efficiency of SNNs implemented in hardware for the
sentiment analysis task compared to traditional deep neural networks, observing
an energy efficiency increase of more than 32x during inference and 60x during
training while incurring the expected energy-performance tradeoff.

通过对比不同的文本编码方法，并在下游的情感分析任务中评估每种方法在相关的脉冲神经网络中的性能，我们提出了一种新的文本编码方法，其在基准的自然语言处理任务上优于一种广泛使用的速率编码技术，峰值率编码，性能提升了约 13%。此外，我们证明了在情感分析任务中，与传统深度神经网络相比，实施在硬件中的脉冲神经网络的能源效率提高了 32 倍以上。