论文标题
DCT-SNN：使用DCT随着时间的推移分发空间信息，以学习低延迟峰值神经网络
DCT-SNN: Using DCT to Distribute Spatial Information over Time for Learning Low-Latency Spiking Neural Networks
论文作者
论文摘要
尖峰神经网络（SNN）为传统深度学习框架提供了一种有希望的替代方法，因为它们由于事件驱动的信息处理提供了更高的计算效率。随着时间的流逝，SNNS将像素强度的模拟值分配到二元尖峰中。但是，最广泛使用的输入编码方案（例如基于泊松的速率编码）不能有效利用SNN的其他时间学习能力。此外，这些SNN遭受了很高的推论潜伏期，这是他们部署的主要瓶颈。为了克服这一点，我们提出了一个可扩展的基于时间的编码方案，该方案利用离散的余弦变换（DCT）减少推理所需的时间段数量。 DCT将图像分解为正弦基础图像的加权总和。在每个时间步骤中，DCT系数的Hadamard乘积和按顺序进行的单个频率基库被赋予在穿过阈值时生成尖峰的累加器。我们使用拟议的方案来学习DCT-SNN，这是一种具有泄漏的整合性和射击神经元的低延迟深SNN，并使用基于替代梯度下降的返回传播训练。我们使用VGG体系结构分别获得了CIFAR-10，CIFAR-100和Tinyimagenet的TOP-1精度，分别为CIFAR-10，CIFAR-100和TINYIMAGENET。值得注意的是，与其他最先进的SNN相比，DCT-SNN的潜伏期减小了2-14倍的推断，同时与标准深度学习对应物的准确性相当。转换的尺寸使我们能够控制推理所需的时间段数量。此外，我们可以通过在推理过程中删除最高频率组件来以有原则的方式进行延迟权衡。
Spiking Neural Networks (SNNs) offer a promising alternative to traditional deep learning frameworks, since they provide higher computational efficiency due to event-driven information processing. SNNs distribute the analog values of pixel intensities into binary spikes over time. However, the most widely used input coding schemes, such as Poisson based rate-coding, do not leverage the additional temporal learning capability of SNNs effectively. Moreover, these SNNs suffer from high inference latency which is a major bottleneck to their deployment. To overcome this, we propose a scalable time-based encoding scheme that utilizes the Discrete Cosine Transform (DCT) to reduce the number of timesteps required for inference. DCT decomposes an image into a weighted sum of sinusoidal basis images. At each time step, the Hadamard product of the DCT coefficients and a single frequency base, taken in order, is given to an accumulator that generates spikes upon crossing a threshold. We use the proposed scheme to learn DCT-SNN, a low-latency deep SNN with leaky-integrate-and-fire neurons, trained using surrogate gradient descent based backpropagation. We achieve top-1 accuracy of 89.94%, 68.3% and 52.43% on CIFAR-10, CIFAR-100 and TinyImageNet, respectively using VGG architectures. Notably, DCT-SNN performs inference with 2-14X reduced latency compared to other state-of-the-art SNNs, while achieving comparable accuracy to their standard deep learning counterparts. The dimension of the transform allows us to control the number of timesteps required for inference. Additionally, we can trade-off accuracy with latency in a principled manner by dropping the highest frequency components during inference.
