Summary of Energy-efficient Spiking Recurrent Neural Network For Gesture Recognition on Embedded Gpus, by Marzieh Hassanshahi Varposhti et al.
Energy-Efficient Spiking Recurrent Neural Network for Gesture Recognition on Embedded GPUs
by Marzieh Hassanshahi Varposhti, Mahyar Shahsavari, Marcel van Gerven
First submitted to arxiv on: 23 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a novel approach to deploying AI algorithms on event-based embedded devices for real-time processing of data. The researchers implement a spiking recurrent neural network (SRNN) with liquid time constant neurons for gesture recognition, showcasing the energy efficiency and computational efficacy of NVIDIA Jetson Nano embedded GPU platforms. The study demonstrates a 14-fold increase in power efficiency compared to conventional GPUs, making it suitable for energy-constrained applications. Additionally, batch processing is found to significantly boost frame rates across various batch sizes while maintaining accuracy levels above the baseline. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers has developed an innovative way to use artificial intelligence (AI) on small devices that can process data in real-time. They used a special type of AI called a spiking recurrent neural network (SRNN) to recognize hand gestures. The SRNN is run on a small computer chip, which is more energy-efficient than traditional computers. This means the device can work for longer without needing to be recharged. The study shows that by processing data in batches, the device can perform tasks even faster while still being accurate. |
Keywords
» Artificial intelligence » Gesture recognition » Neural network
