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04/17/2023
TeAAL: A Declarative Framework for Modeling Sparse Tensor Accelerators
Over the past few years, the explosion in sparse tensor algebra workload...
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03/20/2023
Exploiting Inter-Operation Data Reuse in Scientific Applications using GOGETA
HPC applications are critical in various scientific domains ranging from...
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01/25/2023
Flexagon: A Multi-Dataflow Sparse-Sparse Matrix Multiplication Accelerator for Efficient DNN Processing
Sparsity is a growing trend in modern DNN models. Existing Sparse-Sparse...
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06/07/2022
A Formalism of DNN Accelerator Flexibility
The high efficiency of domain-specific hardware accelerators for machine...
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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...
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01/21/2022
Enabling Flexibility for Sparse Tensor Acceleration via Heterogeneity
Recently, numerous sparse hardware accelerators for Deep Neural Networks...
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01/12/2021
Self-Adaptive Reconfigurable Arrays (SARA): Using ML to Assist Scaling GEMM Acceleration
With increasing diversity in Deep Neural Network(DNN) models in terms of...
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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...
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05/04/2018
MAESTRO: An Open-source Infrastructure for Modeling Dataflows within Deep Learning Accelerators
We present MAESTRO, a framework to describe and analyze CNN dataflows, a...
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04/18/2018
UCNN: Exploiting Computational Reuse in Deep Neural Networks via Weight Repetition
Convolutional Neural Networks (CNNs) have begun to permeate all corners ...
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08/26/2016
