10 citations · 14 across the 4 of their papers we have counts for
4 papers
DLAS: An Exploration and Assessment of the Deep Learning Acceleration Stack
Perry Gibson, José Cano, Elliot J. Crowley +2
Deep Neural Networks (DNNs) are extremely computationally demanding, which presents a large barrier to their deployment on resource-constrained devices. Since such devices are wher…
mlirSynth: Automatic, Retargetable Program Raising in Multi-Level IR using Program Synthesis
Alexander Brauckmann, Elizabeth Polgreen, Tobias Grosser +1
MLIR is an emerging compiler infrastructure for modern hardware, but existing programs cannot take advantage of MLIR's high-performance compilation if they are described in lower-l…
Rewriting History: Repurposing Domain-Specific CGRAs
Jackson Woodruff, Thomas Koehler, Alexander Brauckmann +3
Coarse-grained reconfigurable arrays (CGRAs) are domain-specific devices promising both the flexibility of FPGAs and the performance of ASICs. However, with restricted domains come…
Matching Linear Algebra and Tensor Code to Specialized Hardware Accelerators
Pablo Antonio Martínez, Jackson Woodruff, Jordi Armengol-Estapé +3
Dedicated tensor accelerators demonstrate the importance of linear algebra in modern applications. Such accelerators have the potential for impressive performance gains, but requir…