Uncrewed Systems Technology 047 l Aergility ATLIS l AI focus l Clevon 1 UGV l Geospatial insight l Intergeo 2022 report l AUSA 2022 report I Infinity fuel cell l BeeX A.IKANBILIS l Propellers focus I Phoenix Wings Orca

42 Wind turbine simulation One simulator was originally developed as a cross-platform open source system that combines the Unreal and Unity 3D display engines with popular flight controllers such as PX4 and ArduPilot, and hardware-in- loop with PX4 for physically and visually realistic simulations. Using cloud computing, ML models can run through millions of flights in seconds, learning how to react to a wide range of variables. This synthetic data can be used to look at how a UAV would fly in rain, sleet or snow and how strong winds or high temperatures would affect battery life. December/January 2023 | Uncrewed Systems Technology A neuromorphic, or spiking neural network (SNN), is a different design of AI. It is event-driven, so it is suitable for detecting images infrequently, typically once a second, rather than a video with a frame rate of 30 or 60 frames per second. The latest processor design for spiking neural networks uses six microprocessor cores, up from three, and 128 fully asynchronous neuron cores connected by a network-on- chip. The neuron cores are optimised for neuromorphic workloads, each implementing a group of spiking neurons, including all the connections between the neurons. All the comms between the neuron cores is in the form of asynchronous spike messages, which are triggered only by an event. The processor cores are optimised for spike-based comms and execute standard C code to assist with data I/O as well as network configuration, management and monitoring. Parallel I/O interfaces extend the on-chip mesh across multiple chips – up to 16,384 – with direct pin-to-pin wiring between neighbours. A spike I/O module at the edge of the chip provides configurable hardware- accelerated expansion and encoding of input data into spike messages, reducing the bandwidth required from the external interface and improving performance while reducing the load on the embedded processors. Hardware such as a field programmable gate array can be used to convert the asynchronous output of the SNN to the synchronous output needed by standard interfaces such as Ethernet. SDNN The asynchronous nature of spiking neural networks requires different software tools and libraries to deep neural networks (DNNs), which can make moving from one to the other a challenge. The hardware has therefore been adapted for a DNN implementation known as the Sigma-Delta Neural Network (SDNN). This provides faster detection and higher efficiency than the SNN approach commonly used. SDNNs compute weights in the same way as conventional DNNs, but they only communicate significant changes as they happen, in a sparse and event- driven manner. Simulations have shown that SDNNs can improve the performance of DNN workloads by more than 10 times in both inference speeds and energy efficiency. Rather than optimising for a specific SNN or SDNN model, the hardware implements neuron models with a programmable pipeline in each neuromorphic core to support common arithmetic, comparison and program control flow instructions. This programmability greatly expands its range of neuron models using a dedicated software tool. This software tool is an open, modular and extensible framework for spiking AI designs that allows engineers to converge on a common set of tools, methods and libraries. It runs on existing mainstream and neuromorphic processors, enabling cross-platform execution and interoperability with a variety of AI, neuromorphic and robotics frameworks. That allows developers to start neuromorphic applications without access to specialised neuromorphic hardware, and then running on specialist chips and boards when they become available. One of the first of these hardware systems is a stackable eight-chip system board with an Ethernet interface. It is aimed at portable projects with low power consumption such as UAVs with exposed general- purpose I/O pins and standard synchronous and asynchronous interfaces for integration with sensors and actuators. The boards can be stacked to create larger systems in multiples of eight chips. Neuromorphic AI The Liohi2 neuromorphic chip uses a spiking neural network to process sensor data (Courtesy of Intel)