125 citations · 270 across the 10 of their papers we have counts for
10 papers
HighLight: Efficient and Flexible DNN Acceleration with Hierarchical Structured Sparsity
Yannan Nellie Wu, Po-An Tsai, Saurav Muralidharan +3
Due to complex interactions among various deep neural network (DNN) optimization techniques, modern DNNs can have weights and activations that are dense or sparse with diverse spar…
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…