RRAM-based Low-Power Neuromorphic Computing Engine for Space Applications

With recent breakthroughs in AI and deep learning, applying these techniques to on-board computers for space applications has grown in interest to engineers on space applications. The space field brings its own challenges, such as reliability and power restrictions. The proposed solution in this work concerns a neuromorphic accelerator for a...

High Throughput Parallel Computation with High Bandwidth Memory on FPGA

With the increase of available storage bandwidth, CPUs can not keep up with the compute throughput needed to process this amount of incoming data. GPUs and FPGAs are generally better suited for such tasks. To assist FPGAs in their functions, some boards are equipped with one or more high bandwidth memory (HBM) stacks, with a bandwidth of 230 GB...

Transparently Accelerating Spark SQL Code on Computing Hardware

Through new digital business models, the importance of big data analytics continuously grows. Initially, data analytics clusters were mainly bounded by the throughput of network links and the performance of I/O operations. With current hardware development, this has changed, and often the performance of CPUs and memory access became the new...

Scaling up data analytics in Python using multiple FPGAs

Big data applications are becoming more commonplace due to an abundance of digital data and increasingly powerful hardware. One of these classes of hardware devices are FPGAs, which are being used today in various ways such as data centers and embedded systems. High performance, power efficiency, and reprogrammability are the primary reasons...

Arrow Prefetching: Prefetching Techniques for High Performance Big Data Processors using the Apache Arrow Data Format

As the digitisation of the world progresses at an accelerating pace, an overwhelming quantity of data from a variety of sources, of different types, organised in a multitude of forms or not at all, are subjected into diverse analytic processes for specific kinds of value to be extracted out of them. The aggregation of these analytic processes...

High-Throughput Big Data Analytics Through Accelerated Parquet to Arrow Conversion

With the advent of high-bandwidth non-volatile storage devices, the classical assumption that database analytics applications are bottlenecked by CPUs having to wait for slow I/O devices is being flipped around. Instead, CPUs are no longer able to decompress and deserialize the data stored in storage-focused file formats fast enough to keep up...

Enabling FPGA Memory Management for Big Data Applications Using Fletcher

vailability of FPGAs is increasing due to cloud service offerings. In the wake of a new in-memory storage format specification, Apache Arrow, FPGAs are increasingly interesting for data processing acceleration in the big data domain. The Fletcher framework can be used to easily develop FPGA accelerated applications that access data stored in...