16 citations · 45 across the 8 of their papers we have counts for
16 papers
Stardust: Compiling Sparse Tensor Algebra to a Reconfigurable Dataflow Architecture
Olivia Hsu, Alexander Rucker, Tian Zhao +2
We introduce Stardust, a compiler that compiles sparse tensor algebra to reconfigurable dataflow architectures (RDAs). Stardust introduces new user-provided data representation and…
Efficient Memory Partitioning in Software Defined Hardware
Matthew Feldman, Tian Zhao, Kunle Olukotun
As programmers turn to software-defined hardware (SDH) to maintain a high level of productivity while programming hardware to run complex algorithms, heavy-lifting must be done by…
Capstan: A Vector RDA for Sparsity
Alexander Rucker, Matthew Vilim, Tian Zhao +3
This paper proposes Capstan: a scalable, parallel-patterns-based, reconfigurable dataflow accelerator (RDA) for sparse and dense tensor applications. Instead of designing for one a…
Bayesian Optimization with a Prior for the Optimum
Artur Souza, Luigi Nardi, Leonardo B. Oliveira +3
While Bayesian Optimization (BO) is a very popular method for optimizing expensive black-box functions, it fails to leverage the experience of domain experts. This causes BO to was…
Serving Recurrent Neural Networks Efficiently with a Spatial Accelerator
Tian Zhao, Yaqi Zhang, Kunle Olukotun
Recurrent Neural Network (RNN) applications form a major class of AI-powered, low-latency data center workloads. Most execution models for RNN acceleration break computation graphs…
Efficient Multiway Hash Join on Reconfigurable Hardware
Kunle Olukotun, Raghu Prabhakar, Rekha Singhal +2
We propose the algorithms for performing multiway joins using a new type of coarse grain reconfigurable hardware accelerator~-- ``Plasticine''~-- that, compared with other accelera…