Few-Shot Transfer Learning for Individualized Braking Intent Detection on Neuromorphic Hardware
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866913565333520384 |
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| author | Lutes, Nathan Nadendla, Venkata Sriram Siddhardh Krishnamurthy, K. |
| author_facet | Lutes, Nathan Nadendla, Venkata Sriram Siddhardh Krishnamurthy, K. |
| contents | Objective: This work explores use of a few-shot transfer learning method to train and implement a convolutional spiking neural network (CSNN) on a BrainChip Akida AKD1000 neuromorphic system-on-chip for developing individual-level, instead of traditionally used group-level, models using electroencephalographic data. Main Results: Efficacy of the above methodology to develop individual-specific braking intention predictive models by rapidly adapting the group-level model in as few as three training epochs while achieving at least 90% accuracy, true positive rate and true negative rate is presented. Further, results show the energy-efficiency of the neuromorphic hardware through a power reduction of over 97% with only a $1.3* increase in latency when using the Akida AKD1000 processor for network inference compared to an Intel Xeon central processing unit. Similar results were obtained in a subsequent ablation study using a subset of five out of 19 channels. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_03336 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Few-Shot Transfer Learning for Individualized Braking Intent Detection on Neuromorphic Hardware Lutes, Nathan Nadendla, Venkata Sriram Siddhardh Krishnamurthy, K. Neural and Evolutionary Computing Machine Learning Signal Processing Objective: This work explores use of a few-shot transfer learning method to train and implement a convolutional spiking neural network (CSNN) on a BrainChip Akida AKD1000 neuromorphic system-on-chip for developing individual-level, instead of traditionally used group-level, models using electroencephalographic data. Main Results: Efficacy of the above methodology to develop individual-specific braking intention predictive models by rapidly adapting the group-level model in as few as three training epochs while achieving at least 90% accuracy, true positive rate and true negative rate is presented. Further, results show the energy-efficiency of the neuromorphic hardware through a power reduction of over 97% with only a $1.3* increase in latency when using the Akida AKD1000 processor for network inference compared to an Intel Xeon central processing unit. Similar results were obtained in a subsequent ablation study using a subset of five out of 19 channels. |
| title | Few-Shot Transfer Learning for Individualized Braking Intent Detection on Neuromorphic Hardware |
| topic | Neural and Evolutionary Computing Machine Learning Signal Processing |
| url | https://arxiv.org/abs/2408.03336 |