While FPGAs have seen prior use in database systems, in recent years interest in using
FPGA to accelerate databases has declined in both industry and academia for the following …

This paper addresses the challenge of truth discovery from noisy social sensing data. The
work is motivated by the emergence of social sensing as a data collection paradigm of …

Reconfigurable architectures can bring unique capabilities to computational tasks. They
offer the performance and energy efficiency of hardware with the flexibility of software. In …

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

As the amount of memory in database systems grows, entire database tables, or even
databases, are able to fit in the system's memory, making in-memory database operations …

In the presence of exponential growth of the data produced every day in volume, velocity,
and variety, online analytical processing (OLAP) is becoming increasingly challenging …

The importance of Sparse Matrix dense Vector multiplication (SpMV) operation in graph
analytics and numerous scientific applications has led to development of custom …

As the size of data generated every day grows dramatically, the computational bottleneck of
computer systems has been shifted toward the storage devices. Thanks to recent …

The continuous growth of main memory size allows modern data systems to process entire
large scale datasets in memory. The increase in memory capacity, however, is not matched …

Sorting is a key computational kernel in many big data applications. Most sorting
implementations focus on a specific input size, record width, and hardware configuration …