36 citations · 37 across the 4 of their papers we have counts for
6 papers
ReuseSense: With Great Reuse Comes Greater Efficiency; Effectively Employing Computation Reuse on General-Purpose CPUs
Nitesh Narayana GS, Marc Ordoñez, Lokananda Hari +2
Deep Neural Networks (DNNs) are the de facto algorithm for tackling cognitive tasks in real-world applications such as speech recognition and natural language processing. DNN infer…
Exploiting Kernel Compression on BNNs
Franyell Silfa, Jose Maria Arnau, Antonio González
Binary Neural Networks (BNNs) are showing tremendous success on realistic image classification tasks. Notably, their accuracy is similar to the state-of-the-art accuracy obtained b…
Saving RNN Computations with a Neuron-Level Fuzzy Memoization Scheme
Franyell Silfa, Jose-Maria Arnau, Antonio González
Recurrent Neural Networks (RNNs) are a key technology for applications such as automatic speech recognition or machine translation. Unlike conventional feed-forward DNNs, RNNs reme…
E-BATCH: Energy-Efficient and High-Throughput RNN Batching
Franyell Silfa, Jose Maria Arnau, Antonio Gonzalez
Recurrent Neural Network (RNN) inference exhibits low hardware utilization due to the strict data dependencies across time-steps. Batching multiple requests can increase throughput…
Boosting LSTM Performance Through Dynamic Precision Selection
Franyell Silfa, Jose-Maria Arnau, Antonio Gonzàlez
The use of low numerical precision is a fundamental optimization included in modern accelerators for Deep Neural Networks (DNNs). The number of bits of the numerical representation…
E-PUR: An Energy-Efficient Processing Unit for Recurrent Neural Networks
Franyell Silfa, Gem Dot, Jose-Maria Arnau +1
Recurrent Neural Networks (RNNs) are a key technology for emerging applications such as automatic speech recognition, machine translation or image description. Long Short Term Memo…