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...

Improving the usability and scalability of FINN, a DNN compiler for FPGAs

FINN is a framework developed by Xilinx Research Labs that compiles Deep Neural Network software descriptions into fast and scalable dataflow architectures for inference acceleration on FPGAs. The dataflow<br/>architectures are network dependent, sized according to the user-defined throughput requirements, and constrained by available resources...

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...