2 citations · 4 across the 8 of their papers we have counts for
9 papers
Towards Efficient LUT-based PIM: A Scalable and Low-Power Approach for Modern Workloads
Bahareh Khabbazan, Marc Riera, Antonio González
Data movement in memory-intensive workloads, such as deep learning, incurs energy costs that are over three orders of magnitude higher than the cost of computation. Since these wor…
Hamun: An Approximate Computation Method to Prolong the Lifespan of ReRAM-Based Accelerators
Mohammad Sabri, Marc Riera, Antonio Gonzalez
ReRAM-based accelerators exhibit enormous potential to increase computational efficiency for DNN inference tasks, delivering significant performance and energy savings over traditi…
ARAS: An Adaptive Low-Cost ReRAM-Based Accelerator for DNNs
Mohammad Sabri, Marc Riera, Antonio González
Processing Using Memory (PUM) accelerators have the potential to perform Deep Neural Network (DNN) inference by using arrays of memory cells as computation engines. Among various m…
An Energy-Efficient Near-Data Processing Accelerator for DNNs that Optimizes Data Accesses
Bahareh Khabbazan, Marc Riera, Antonio González
The constant growth of DNNs makes them challenging to implement and run efficiently on traditional compute-centric architectures. Some accelerators have attempted to add more compu…
ReDy: A Novel ReRAM-centric Dynamic Quantization Approach for Energy-efficient CNN Inference
Mohammad Sabri, Marc Riera, Antonio González
The primary operation in DNNs is the dot product of quantized input activations and weights. Prior works have proposed the design of memory-centric architectures based on the Proce…
DNA-TEQ: An Adaptive Exponential Quantization of Tensors for DNN Inference
Bahareh Khabbazan, Marc Riera, Antonio González
Quantization is commonly used in Deep Neural Networks (DNNs) to reduce the storage and computational complexity by decreasing the arithmetical precision of activations and weights,…