Neural network partitioning for resource-limited environments

The demand for implementing neural networks on edge devices has rapidly increased as they allow designers to move away from expensive server-grade hardware. However, due to the limited resources available on edge devices, it is challenging to implement complex neural networks. This study selected the Kria SoM KV260 hardware platform due to its...

Optimizing Memory Mapping for Dataflow Inference Accelerators: Efficient Memory Utilization on FPGAs

Convolutional Neural Network (CNN) inference has gained a significant amount of traction for performing tasks like speech recognition and image classification. To improve the accuracy with which these tasks can be performed, CNNs are typically designed to be deep, encompassing a large number of neural network layers. As a result, the...

Faster than the Speed of Life: Accelerating Developmental Biology Simulations with GPUs and FPGAs

Life scientists are faced with the tough challenge of developing high-performance computer simulations of their increasingly complex models. BioDynaMo is an open-source biological simulation platform that aims to alleviate them from the intricacies that go into development. Life scientists are able to base their models on top of BioDynaMo’s...

Multi-way Hash Join Based on FPGAs

The multi-way hash join is one of the commonly used and time-consuming database operations. Many algorithms have been developed to accelerate this operation, some of which use accelerators such as field programmable gate arrays (FPGAs). However, most of the previous work was focused on computation-intensive operations such as (de)compression,...