Spiking-YOLO: Spiking Neural Network for Energy-Efficient Object Detection

By Seijoon Kim, Seongsik Park, Byunggook Na, Sungroh Yoon. AAAI Conference on Artificial Intelligence, 2019

538 citations recorded by the source corpus.

Over the past decade, deep neural networks (DNNs) have demonstrated remarkable performance in a variety of applications. As we try to solve more advanced problems, increasing demands for computing and power resources has become inevitable. Spiking neural networks (SNNs) have attracted widespread interest as the third-generation of neural networks due to their event-driven and low-powered nature. SNNs, however, are difficult to train, mainly owing to their complex dynamics of neurons and non-differentiable spike operations. Furthermore, their applications have been limited to relatively simple tasks such as image classification. In this study, we investigate the performance degradation of SNNs in a more challenging regression problem (i.e., object detection). Through our in-depth analysis, we introduce two novel methods: channel-wise normalization and signed neuron with imbalanced threshold, both of which provide fast and accurate information transmission for deep SNNs. Consequently, we present a first spiked-based object detection model, called Spiking-YOLO. Our experiments show that Spiking-YOLO achieves remarkable results that are comparable (up to 98%) to those of Tiny YOLO on non-trivial datasets, PASCAL VOC and MS COCO. Furthermore, Spiking-YOLO on a neuromorphic chip consumes approximately 280 times less energy than Tiny YOLO and converges 2.3 to 4 times faster than previous SNN conversion methods.

Subfields: Cognitive Neuroscience, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/71b411d47ae7b9cf8f6ff26dc2eda8e5038ef94c

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

1. Yuhang Li
2. Zhaofei Yu
3. Shi-Wee Deng
4. Shi Gu
5. Wei Fang
6. Yanqing Chen
7. Shanghang Zhang
8. Timothée Masquelier

In Electrical and Electronic Engineering

1. Yuhang Li
2. Zhaofei Yu
3. Shi-Wee Deng
4. Shi Gu
5. Wei Fang
6. Yanqing Chen
7. Shanghang Zhang
8. Timothée Masquelier

Citations in the corpus

• SpikingJelly: An open-source machine learning infrastructure platform for spike-based intelligence (2023, methodology): “Because of the rapid progress achieved by deep learning methods, the applications of SNNs have been expanded beyond classification to other tasks including object detection ( 64 – 66 ), object segmentation ( 67 , 68 ), depth estimation ( 69 ), and optical flow estimation ( 70 ).”
• Temporal Efficient Training of Spiking Neural Network via Gradient Re-weighting (2022, methodology): “Some special techniques have been proposed to reduce the inference latency, such as the subtraction mechanism (Rueckauer et al., 2016; Han et al., 2020), robust normalization Rueckauer et al. (2016), spike-norm (Sengupta et al., 2018), and channel-wise normalization (Kim et al., 2019).”