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...

Investigating scaling techniques and the cost-efficiency of distributed to single FPGA compositions for Full Waveform Inversion

In recent years, the big data era has produced an increasing volume and complexity of data that requires processing. To analyze and process these large amounts of data, applications are being scaled on large clusters using distributed data processing frameworks. A more recent trend utilizes hardware accelerators to offload computationally...

Integration of a convolutional neural network for speech-to-text recognition in an FPGA compiler flow

Deep Neural Network (DNNs) have increased significantly in size over the past decade. Partly due to this, the accuracy of DNNs in image classification and speech recognition tasks has increased as well. This enables a great potential for such models to be applied in real-world applications. However, due to their size, the compute and power...

Ultra low latency deep neural network inference for gravitational waves interferometer: *

Low latency Convolutional Neural Network (CNN) inference research is gaining more and more momentum for tasks such as speech and image classications. This is because CNNs have the ability to surpass human accuracy in classication of images. For improving the measurement setup of gravitational waves, low latency CNNs inference are researched. The...