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Exploiting Beam Search Confidence for Energy-Efficient Speech Recognition
With computers getting more and more powerful and integrated in our dail...
01/22/2021 ∙ by Dennis Pinto, et al. ∙
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E-BATCH: Energy-Efficient and High-Throughput RNN Batching
Recurrent Neural Network (RNN) inference exhibits low hardware utilizati...
09/22/2020 ∙ by Franyell Silfa, et al. ∙
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Irregular Accesses Reorder Unit: Improving GPGPU Memory Coalescing for Graph-Based Workloads
GPGPU architectures have become established as the dominant parallelizat...
07/14/2020 ∙ by Albert Segura, et al. ∙
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Boosting LSTM Performance Through Dynamic Precision Selection
The use of low numerical precision is a fundamental optimization include...
11/07/2019 ∙ by Franyell Silfa, et al. ∙
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LSTM-Sharp: An Adaptable, Energy-Efficient Hardware Accelerator for Long Short-Term Memory
The effectiveness of LSTM neural networks for popular tasks such as Auto...
11/04/2019 ∙ by Reza Yazdani, et al. ∙
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(Pen-) Ultimate DNN Pruning
DNN pruning reduces memory footprint and computational work of DNN-based...
06/06/2019 ∙ by Marc Riera, et al. ∙
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E-PUR: An Energy-Efficient Processing Unit for Recurrent Neural Networks
Recurrent Neural Networks (RNNs) are a key technology for emerging appli...
11/20/2017 ∙ by Franyell Silfa, et al. ∙
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