125 citations · 270 across the 10 of their papers we have counts for
5 papers · 1 filter
Demystifying Map Space Exploration for NPUs
Sheng-Chun Kao, Angshuman Parashar, Po-An Tsai +1
Map Space Exploration is the problem of finding optimized mappings of a Deep Neural Network (DNN) model on an accelerator. It is known to be extremely computationally expensive, an…
A Formalism of DNN Accelerator Flexibility
Sheng-Chun Kao, Hyoukjun Kwon, Michael Pellauer +2
The high efficiency of domain-specific hardware accelerators for machine learning (ML) has come from specialization, with the trade-off of less configurability/ flexibility. There…
Sparseloop: An Analytical Approach To Sparse Tensor Accelerator Modeling
Yannan Nellie Wu, Po-An Tsai, Angshuman Parashar +2
In recent years, many accelerators have been proposed to efficiently process sparse tensor algebra applications (e.g., sparse neural networks). However, these proposals are single…
DiGamma: Domain-aware Genetic Algorithm for HW-Mapping Co-optimization for DNN Accelerators
Sheng-Chun Kao, Michael Pellauer, Angshuman Parashar +1
The design of DNN accelerators includes two key parts: HW resource configuration and mapping strategy. Intensive research has been conducted to optimize each of them independently.…
Enabling Flexibility for Sparse Tensor Acceleration via Heterogeneity
Eric Qin, Raveesh Garg, Abhimanyu Bambhaniya +5
Recently, numerous sparse hardware accelerators for Deep Neural Networks (DNNs), Graph Neural Networks (GNNs), and scientific computing applications have been proposed. A common ch…