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Found 5 papers, 0 papers with code
Exploiting Kernel Compression on BNNs
In this regard, BNNs are very amenable to edge devices since they employ 1-bit to store the inputs and weights, and thus, their storage requirements are low.
Saving RNN Computations with a Neuron-Level Fuzzy Memoization Scheme
In this paper, we observe that the output of a neuron exhibits small changes in consecutive invocations.~We exploit this property to build a neuron-level fuzzy memoization scheme, which dynamically caches each neuron's output and reuses it whenever it is predicted that the current output will be similar to a previously computed result, avoiding in this way the output computations.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
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.
Boosting LSTM Performance Through Dynamic Precision Selection
Based on this observation, we implement a novel hardware scheme that tracks the evolution of the elements in the LSTM cell state and dynamically selects the appropriate precision in each time step.
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
