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Found 5 papers, 0 papers with code
CREW: Computation Reuse and Efficient Weight Storage for Hardware-accelerated MLPs and RNNs
To this end, each weight is replaced offline by an index in the buffer of unique products.
E-BATCH: Energy-Efficient and High-Throughput RNN Batching
However, RNN batching requires a large amount of padding since the batched input sequences may largely differ in length.
SHARP: An Adaptable, Energy-Efficient Accelerator for Recurrent Neural Network
To solve these issues, we propose an intelligent tiled-based dispatching mechanism for increasing the adaptiveness of RNN computation, in order to efficiently handle the data dependencies.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
(Pen-) Ultimate DNN Pruning
DNN pruning reduces memory footprint and computational work of DNN-based solutions to improve performance and energy-efficiency.
E-PUR: An Energy-Efficient Processing Unit for Recurrent Neural Networks
The main goal of E-PUR is to support large recurrent neural networks for low-power mobile devices.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
