1 citations · 2 across the 5 of their papers we have counts for
5 papers
Analyzing and Improving Hardware Modeling of Accel-Sim
Rodrigo Huerta, Mojtaba Abaie Shoushtary, Antonio González
GPU architectures have become popular for executing general-purpose programs. Their many-core architecture supports a large number of threads that run concurrently to hide the late…
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…
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,…