Sparse matrices are widely used in graph and data analytics, machine learning, engineering
and scientific applications. This paper describes and analyzes OuterSPACE, an accelerator …

Sparse matrix-sparse matrix multiplication (spMspM) is at the heart of a wide range of
scientific and machine learning applications. spMspM is inefficient on general-purpose …

Increasing demand for power-efficient, high-performance computing has spurred a growing
number and diversity of hardware accelerators in mobile and server Systems on Chip …

Accelerators spend significant area and effort on custom on-chip buffering. Unfortunately,
these solutions are strongly tied to particular designs, hampering re-usability across other …

This paper introduces a new resource virtualization framework, Zorua, that decouples the
programmer-specified resource usage of a GPU application from the actual allocation in the …

This paper proposes Capstan: a scalable, parallel-patterns-based, reconfigurable dataflow
accelerator (RDA) for sparse and dense tensor applications. Instead of designing for one …

As GPUs have become increasingly general purpose, applications with more general
sharing patterns and fine-grained synchronization have started to emerge. Unfortunately …

Dynamic adaptation is a post-silicon optimization technique that adapts the hardware to
workload phases. However, current adaptive approaches are oblivious to implicit phases …

Cache hierarchies are increasingly non-uniform and difficult to manage. Several techniques,
such as scratchpads or reuse hints, use static information about how programs access data …

Graphics Processing Units (GPUs) are widely-used accelerators for data-parallel
applications. In many GPU applications, GPU memory bandwidth bottlenecks performance …