Exploiting deep learning accelerators for neuromorphic workloads
Spiking neural networks (SNNs) have achieved orders of magnitude improvement in terms of energy consumption and latency when performing inference with deep learning workloads. Error backpropagation is presently regarded as the most effective method for training SNNs, but in a twist of irony, when tr...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IOP Publishing
2024-01-01
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| Series: | Neuromorphic Computing and Engineering |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/2634-4386/ad2373 |