Spiking-YOLO: Spiking Neural Network for Energy-Efficient Object Detection
Authors: Seijoon Kim, Seongsik Park, Byunggook Na, Sungroh Yoon11270-11277
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 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. |
| Researcher Affiliation | Academia | Seijoon Kim, Seongsik Park, Byunggook Na, Sungroh Yoon Department of Electrical and Computer Engineering, ASRI, INMC, and Institute of Engineering Research Seoul National University, Seoul 08826, Korea sryoon@snu.ac.kr |
| Pseudocode | Yes | Algorithm 1: Channel-wise normalization |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or include links to a code repository. |
| Open Datasets | Yes | 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. |
| Dataset Splits | No | No explicit percentages or sample counts for training/validation/test splits are provided. The paper mentions training on 'PASCAL VOC and MS COCO' and states 'maximum activation is calculated from the training dataset', but does not specify how the datasets were split into train, validation, and test sets to ensure reproducibility. |
| Hardware Specification | Yes | Our simulation is based on the Tensor Flow Eager and we conducted all experiments on NVIDIA Tesla V100 GPUs. |
| Software Dependencies | No | Our simulation is based on the Tensor Flow Eager. The paper does not provide specific version numbers for TensorFlow Eager or any other software libraries. |
| Experiment Setup | Yes | For further analysis, we performed additional experiments on two different output decoding schemes: one based on accumulated Vmem, and another based on spike count. |