Graph analytics are an emerging class of irregular applications. Operating on very large
datasets, they present unique behaviors, such as fine-grained, unpredictable memory …

Conventional High-Level Synthesis (HLS) tools exploit parallelism mostly at the Instruction
Level (ILP). They statically schedule the input specifications and build centralized Finite …

Edge systems are required to autonomously make real-time decisions based on large
quantities of input data under strict power, performance, area, and other constraints. Meeting …

In this paper we present a set of techniques that enable the synthesis of efficient custom
accelerators for memory intensive, irregular applications. To address the challenges of …

RDF databases naturally map to a graph representation and employ languages, such as
SPARQL, that implements queries as graph pattern matching routines. Graph methods …

Conventional High Level Synthesis (HLS) tools mainly target compute intensive kernels
typical of digital signal processing applications. We are develo** techniques and …

Next generation systems, such as edge devices, will need to provide efficient processing of
machine learning (ML) algorithms along several metrics, including energy, performance …

Data analytics applications increasingly are complex workflows composed of phases with
very different program behaviors (eg, graph algorithms and machine learning, algorithms …

High-Level-Synthesis can be used to generate hardware accelerators for compute intense
software parts (so called kernels). For meaningful acceleration, such kernels should be able …

Chips become more complex so it is so important to have design automation in the upper
level of abstraction, where the trade-off is more effective and functionality is simpler for …