With the end of both Dennard's scaling and Moore's law, computer users and researchers
are aggressively exploring alternative forms of computing in order to continue the …

Domain-specific hardware accelerators can provide orders of magnitude speedup and
energy efficiency over general purpose processors. However, they require extensive manual …

Machine learning (ML) models are widely used in many important domains. For efficiently
processing these computational-and memory-intensive applications, tensors of these …

Coarse-grain reconfigurable arrays (CGRAs) can achieve much higher performance and
efficiency than general-purpose cores, approaching the performance of a specialized design …

Because of the importance of graph workloads and the limitations of CPUs/GPUs, many
graph processing accelerators have been proposed. The basic approach of prior …

FPGAs have been proven to be powerful computational accelerators across many types of
workloads. The mainstream programming approach is high level synthesis (HLS), which …

Emerging sensing applications create an unprecedented need for energy efficiency in
programmable processors. To achieve useful multi-year deployments on a small battery or …

We propose the Sparse Abstract Machine (SAM), an abstract machine model for targeting
sparse tensor algebra to reconfigurable and fixed-function spatial dataflow accelerators …

Sparse tensor algorithms are becoming widespread, particularly in the domains of deep
learning, graph and data analytics, and scientific computing. Current high-performance …

Reconfigurable accelerators, like CGRAs and dataflow architectures, have come to
prominence for addressing data-processing problems. However, they are largely limited to …