Data-intensive workloads and applications, such as machine learning (ML), are fundamentally limited by traditional computing systems based on the von-Neumann architecture. As data movement operations and energy consumption ...

Recurrent neural networks (RNNs) perform element-wise multiplications across the activations of gates. We show that a significant percentage of activations are saturated and propose coarse-grained pruning of activations ...

Deep Neural Networks (DNNs) have achieved tremendous success for cognitive applications. The core operation in a DNN is the dot product between quantized inputs and weights. Prior works exploit the weight/input repetition ...

[CATALÀ] Primer s'explicarà breument l'arquitectura d'un MIPS, la jerarquia de memòria i el funcionament de la cache. Posteriorment s'explicarà com s'ha dissenyat i implementat una jerarquia de memòria per a un MIPS ...

DNN pruning reduces memory footprint and computational work of DNN-based solutions to improve performance and energy-efficiency. An effective pruning scheme should be able to systematically remove connections and/or neurons ...

The constant growth of DNNs makes them challenging to implement and run efficiently on traditional computecentric architectures. Some works have attempted to enhance accelerators by adding more compute units and on-chip ...

DNN pruning has been recently proposed as an effective technique to improve the energy-efficiency of DNN-based solutions. It is claimed that by removing unimportant or redundant connections, the pruned DNN delivers higher ...