Spatial computing architectures pose an attractive alternative to mitigate control and data
movement overheads typical of load-store architectures. In practice, these devices are rarely …

The increasing computational and memory requirements of Deep Learning (DL) workloads
has led to outstanding innovations in hardware architectures. An archetype of such …

Abstract Machine Learning algorithms, such as classification and clustering techniques,
have gained significant traction over the last years because they are vital to many real-world …

Multilayer feed-forward Artificial Neural Networks (ANNs) are universal function
approximators capable of modeling measurable functions to any desired degree of …

Abstract Conditional Restricted Boltzmann Machine (CRBM) is a promising candidate for a
multidimensional system modeling that can learn a probability distribution over a set of data …

As smart metering technology evolves, power suppliers can make low-cost, low-risk
estimation of customer-side power consumption by analyzing energy demand data collected …

Matrix multiplication is a fundamental operation in many fields including artificial intelligence
and machine learning, and it often requires significant computational resources. FPGAs …

Accurate and efficient Machine Learning algorithms are of vital importance to many
problems, especially on classification or clustering tasks. One the most important algorithms …

Low-power, high-speed neural networks are critical for providing deployable embedded AI
applications at the edge. We describe a **linx FPGA implementation of Neural Engineering …

When a tsunami occurred on a sea area, prediction of its arrival time is critical for evacuating
people from the coastal area. There are many problems related to tsunami to be solved for …