Our scientific collaborators at the San Diego Zoo Wildlife Alliance have a long running research program studying the behaviors of endangered iguanas in the Caribbean. As part of their efforts to understand these animals, they attach tiny radios to the iguanas and attempt to track them over weeks to months. In the past, this has largely relied on humans equipped with directional antennas traversing rough terrain to find these radios and the iguanas attached to them.

Our Engineers for Exploration researchers felt we could do better. Over the years, we have developed a drone equipped with a software defined radio to fly over an area and find the animals. The software defined radio “listens” for the radios attached to the iguanas, and captures characteristics of each radio’s signal. We have developed automated algorithms that analyze the received data from the drone’s radio to provide an estimate about the location of the iguanas. The algorithm fuses together position estimates from different times and locations. Our field deployments over that past several years have shown that our drone-based system can effectively find radio-tagged animals.

This research was recently published in the Journal of Field Robotics. For more details, please see our paper below. Congrats to all the authors!

Nathan T. Hu, Eric K. Lo, Jen B. Moss, Glenn P. Gerber, Mark E. Welch, Ryan Kastner, and Curt Schurgers, “A More Precise Way to Localize Animals Using Drones“, Journal of Field Robotics, 2021 (pdf)

Spiking Neural Networks (SNNs) utilize an event-based representation to perform more efficient computation than existing artificial neural networks. SNNs show a lot of promise for low energy computation, but are still limited by the lack of quality training tools and efficient hardware implementations.

Our recent work published at the ACM/IEEE International Symposium of Field-Programmable Gate Arrays (ISFPGA) extends the Xilinx FINN architecture to support streaming spiking neural networks (S2N2). S2N2 efficiently supports both axonal and synaptic delays for feedforward networks with interlayer connections. We show that because of the spikes’ binary nature, a binary tensor can be used for addressing the input events of a layer. We show that S2N2 works well for automatic modulation classification — an important problem for modern wireless networks.

The work was done in collaboration with Xilinx. For more details, check out Ali’s talk at ISFPGA

Paper Reference: Alireza Khodamoradi, Kristof Denolf, and Ryan Kastner, “S2N2: A FPGA Accelerator for Streaming Spiking Neural Network“, International Symposium on Field-Programmable Gate Arrays (ISFPGA) (pdf)