research
∙
05/22/2023
HighLight: Efficient and Flexible DNN Acceleration with Hierarchical Structured Sparsity
Due to complex interactions among various deep neural network (DNN) opti...
share
research
∙
10/07/2022
Demystifying Map Space Exploration for NPUs
Map Space Exploration is the problem of finding optimized mappings of a ...
research
∙
06/07/2022
A Formalism of DNN Accelerator Flexibility
The high efficiency of domain-specific hardware accelerators for machine...
research
∙
05/12/2022
Sparseloop: An Analytical Approach To Sparse Tensor Accelerator Modeling
In recent years, many accelerators have been proposed to efficiently pro...
research
∙
01/26/2022
DiGamma: Domain-aware Genetic Algorithm for HW-Mapping Co-optimization for DNN Accelerators
The design of DNN accelerators includes two key parts: HW resource confi...
research
∙
01/21/2022
Enabling Flexibility for Sparse Tensor Acceleration via Heterogeneity
Recently, numerous sparse hardware accelerators for Deep Neural Networks...
research
∙
09/15/2021
Union: A Unified HW-SW Co-Design Ecosystem in MLIR for Evaluating Tensor Operations on Spatial Accelerators
To meet the extreme compute demands for deep learning across commercial ...
research
∙
03/02/2021
Mind Mappings: Enabling Efficient Algorithm-Accelerator Mapping Space Search
Modern day computing increasingly relies on specialization to satiate gr...
research
∙
02/18/2020
Marvel: A Data-centric Compiler for DNN Operators on Spatial Accelerators
The efficiency of a spatial DNN accelerator depends heavily on the compi...
research
∙
05/23/2017
