97 citations · 130 across the 6 of their papers we have counts for
6 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…
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…
SIMD: A Generalized Matrix Instruction Set for Accelerating Tensor Computation beyond GEMM
Yunan Zhang, Po-An Tsai, Hung-Wei Tseng
Matrix-multiplication units (MXUs) are now prevalent in every computing platform. The key attribute that makes MXUs so successful is the semiring structure, which allows tiling for…
Union: A Unified HW-SW Co-Design Ecosystem in MLIR for Evaluating Tensor Operations on Spatial Accelerators
Geonhwa Jeong, Gokcen Kestor, Prasanth Chatarasi +5
To meet the extreme compute demands for deep learning across commercial and scientific applications, dataflow accelerators are becoming increasingly popular. While these "domain-sp…
Mind Mappings: Enabling Efficient Algorithm-Accelerator Mapping Space Search
Kartik Hegde, Po-An Tsai, Sitao Huang +3
Modern day computing increasingly relies on specialization to satiate growing performance and efficiency requirements. A core challenge in designing such specialized hardware archi…