3 citations · 5 across the 8 of their papers we have counts for
5 papers · 1 filter
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…
Dynamic Sampling Rate: Harnessing Frame Coherence in Graphics Applications for Energy-Efficient GPUs
Martí Anglada, Enrique de Lucas, Joan-Manuel Parcerisa +2
In real-time rendering, a 3D scene is modelled with meshes of triangles that the GPU projects to the screen. They are discretized by sampling each triangle at regular space interva…
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…
Mixture-of-Rookies: Saving DNN Computations by Predicting ReLU Outputs
Dennis Pinto, Jose-María Arnau, Antonio González
Deep Neural Networks (DNNs) are widely used in many applications domains. However, they require a vast amount of computations and memory accesses to deliver outstanding accuracy. I…
ASRPU: A Programmable Accelerator for Low-Power Automatic Speech Recognition
Dennis Pinto, Jose-María Arnau, Antonio González
The outstanding accuracy achieved by modern Automatic Speech Recognition (ASR) systems is enabling them to quickly become a mainstream technology. ASR is essential for many applica…