Here is another post by Istanbul-based Alican Kiraz, Head of Cyber Defense Center @Trendyol Group, which links to a Medium article written by him titled “Hybrid SNN Models and Architectures for Causal Reasoning in Next-Generation Military UAVs” that gives us a little theoretical background regarding the PoC he is envisaging, for which he will be utilising his newly purchased Akida Mini PCIe Board:
TR & EN | Selamlar dostlar 🙏 uzun süredir yapay zeka karar-destek sistemleri üzerine kafa yorarken, Hibrit SNN & Causal Reasoning yapay zeka mimari ve yaklaşımıyla Askeri UAV'lerin nasıl evrilebileceği üzerine yeni makalemde teknik ve teorik düzeyde beyin fırtınası yaptım. Özellikle; -...
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(English version)
EN | Hybrid SNN Models and Architectures for Causal Reasoning in Next-Generation Military UAVs
Alican Kiraz
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7 min read
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1 hour ago
I created it with Midjourney
Although the initial emergence and areas of interest for Unmanned Aerial Vehicles (UAVs) were diverse, their most prominent and widespread use today is undoubtedly in military applications. With the rise of Artificial Intelligence systems and technologies over the past five years, this interest has undergone a significant transformation. Platforms such as the Anduril YFQ-44 and Shield AI MQ-35 V-BAT are now capable of executing missions autonomously, being tasked and coordinated by decision-support AI systems.
Artificial Intelligence is increasingly employed in critical missions such as Intelligence, Surveillance, and Reconnaissance (ISR), autonomous navigation, and target identification. However, the deployment of such AI solutions on the field presents significant challenges — both in terms of mission effectiveness and operational security, as well as resource constraints. These limitations often lead to hesitation and caution in real-world use.
You can access the Turkish version of my article here:
alican-kiraz1.medium.com
One of the most critical challenges lies in the high computational and energy demands of conventional Artificial Neural Networks (ANNs) and Convolutional Neural Networks (CNNs), which are widely used in computer vision and autonomous navigation. Additionally, transformer-based Large Language Models (LLMs) operating in an embedded, agentic architecture for decision support further exacerbate these requirements. These model architectures typically necessitate high-performance GPUs or ruggedized onboard supercomputers integrated into the UAV system.
As you may recall, in my previous article, I examined Russia’s Geran-2 drone, which operates with an onboard Nvidia Jetson Orin module. You may also want to revisit that article for further insights.
alican-kiraz1.medium.com
alican-kiraz1.medium.com
The integration of Hybrid Spiking Neural Network (SNN) and Causal Reasoning (CR) architectures offers significant advantages in terms of reduced energy consumption, lower latency for real-time decision-making, and increased system reliability. Let us now conceptualize such a system — CR-SNN — built upon a hybrid architecture combining SNN and CR.
This hybrid system leverages the biologically inspired, event-driven nature of SNNs to achieve energy efficiency and computational speed, especially when deployed on neuromorphic hardware platforms. Meanwhile, the Causal Reasoning (CR) component — grounded in Judea Pearl’s causal inference framework, including Structural Causal Models (SCMs) and do-calculus — enables UAVs to develop enhanced situational awareness in complex scenarios. It empowers autonomous systems to perform tasks such as target tracking more effectively and to produce more generalizable and interpretable outputs, even in novel situations not encountered during training.
Moreover, although current AI-based systems can exhibit high accuracy in controlled environments, they tend to lose output quality and reliability when exposed to the complexities of real-world civilian or battlefield environments. In such conditions, the probability of incorrect responses increases significantly. Especially under dynamic circumstances or in the presence of adversarial attacks targeting the UAV, reactive maneuvers become increasingly challenging.
For instance, distinguishing between civilians and combatants in dense urban environments or detecting camouflaged targets remains a considerable challenge for current AI models. Furthermore, the inability of deep learning models — often considered “black boxes” — to provide transparent reasoning for their decisions undermines the trustworthiness and accountability of weaponized UAVs in military operations.
In addition, processing high-bandwidth sensor data in real-time — such as high-resolution visible & thermal imagery or Synthetic Aperture Radar (SAR) outputs — imposes substantial computational burdens on existing architectures, increasing latency and slowing down decision-making.
Even on popular edge computing platforms like the NVIDIA Jetson Orin Nano, optimized models such as YOLOv8n typically operate at around 10 frames per second (FPS), which falls short of the 30 FPS threshold required for real-time navigation. Additionally, the need for active thermal management and an average power consumption of around 20 watts pose significant challenges concerning weight, energy constraints, and thermal design requirements on UAV platforms.
Source:
https://www.seeedstudio.com/blog/20...eYa20KsdYMhX0iJB8GfkWnDoTWWnMlo74zgTLxHoOA3_A
Source:
https://dataphoenix.info/a-guide-to-the-yolo-family-of-computer-vision-models/
Furthermore, transmitting data to the C4 (Command, Control, Communication, and Computers) center for decision-support purposes and relaying mission directives back to the UAV can introduce end-to-end latencies exceeding 150 milliseconds. Such delays risk causing the UAV to miss its optimal decision-making window, especially in time-sensitive scenarios.
Beyond performance metrics, next-generation military UAVs are expected to fulfill a range of advanced capabilities. These include: achieving higher levels of autonomy, maintaining superior situational awareness in complex and uncertain environments, making faster and more accurate decisions, extending operational range and flight endurance, and participating in joint missions in coordination with manned combat aircraft. Meeting these requirements is critical for future air combat effectiveness.
To address these demands, we must go beyond current AI architectures. There is a pressing need for solutions that are more efficient, faster, more reliable, and fundamentally smarter. Specifically, we require AI systems — both in software and hardware — that combine low power consumption, low latency, and high performance, while also possessing the ability to comprehend and act upon complex causal relationships within dynamic operational contexts.
Source:
https://exoswan.com/is-neuromorphic-computing-the-future-of-ai
At this point, the combination of approaches such as Spiking Neural Networks (SNNs) and Causal Reasoning (CR) presents a promising pathway to overcome these challenges. These hybrid SNN-CR models hold significant potential to deliver strategic advantages for next-generation military UAVs.
Thanks to the temporally encoded and sparsely activated communication mechanisms of SNNs — modeled after biological neural systems — these networks exhibit significantly lower energy consumption compared to traditional Artificial Neural Networks (ANNs), while also enabling faster inference times under real-time operational constraints.
Source:
https://peerj.com/articles/cs-2549/
In a study conducted by researchers at ETH Zurich, it was demonstrated that SNNs consumed six times less energy than a CNN when performing an obstacle avoidance task. Moreover, the SNN model operated with an average inference latency of just 2.4 milliseconds, showcasing its suitability for highly dynamic scenarios.
For further details, see:
https://www.sciencedirect.com/science/article/pii/S0925231223010081
Another critical aspect is Causal Reasoning (CR). When integrated — particularly through the framework proposed by Judea Pearl — CR enables AI systems to reason not only through learned correlations, but also through underlying causal relationships. This allows the model to perform robustly and generalize more effectively in environments that deviate from the training data distribution or when faced with unforeseen conditions.
To illustrate with a practical case: in a mission involving the detection of a human group as a potential target, conventional models may focus on features such as camouflage color, presence of vehicles, or group size. In contrast, a causally-informed model could prioritize more nuanced and domain-relevant cues — such as body height, shadows, camouflage patterns, military-style marching formations, or spacing intervals — along with distinct features like carried weapons, by leveraging appropriate sensor modalities and reasoning mechanisms.
Image Source:
https://www.newscientist.com/
The component I wish to emphasize most — and which I have long researched for integration into this powerful architecture — is Causal Reasoning (CR). CR refers to the process by which an AI system understands and analyzes causal relationships between events, then uses this understanding to make decisions or support decision-making based on those causal inferences.
What distinguishes CR from conventional approaches? Traditional machine learning and large language models (LLMs) primarily rely on statistical correlations or correlation-based inference. By integrating CR, we aim to enable the model to reason through causality — essentially allowing the system to ask itself “why” and to answer that question autonomously. This line of reasoning is grounded in the foundational work of Judea Pearl, who introduced a formal framework for causality. Pearl’s model is built upon concepts such as Structural Causal Models (SCMs), causal graphs, and do-calculus.
So, what can CR contribute to a UAV system? For instance, it can be highly effective in diagnosing system failures. Imagine a scenario where a UAV encounters multiple issues such as GPS malfunction, engine anomalies, or communication loss. A causal model can represent these as nodes in a causal graph, with the edges denoting the causal links between them. This enables the system to perform counterfactual or interventional reasoning, such as:
Or:
Such inferential capabilities are powerful, aren’t they?
Source:
https://www.nature.com/articles/s42256-023-00744-z
I hope this overview has been insightful. In the next section, we will delve into the technical details and adaptation strategies. Additionally, now that the neuromorphic chip — BrainChip Akida — has arrived, I will be working towards establishing a proof of concept (PoC) based on the proposed architecture.
See you soon!
Drones
Military
AI
Neural Networks
Military Drones
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Head of Cyber Defense Center @Trendyol | CSIE | CSAE | CCISO | CASP+ | OSCP | eCIR | CPENT | eWPTXv2 | eCDFP | eCTHPv2 | OSWP | CEH Master | Pentest+ | CySA+
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(for the first paper above in collaboration with two other Abu Dhabi-based researchers from Khalifa University), this new paper - as the title suggests - is more foundational and mentions Akida only once when listing examples of available neuromorphic hardware. It also addresses “key challenges and open research questions”.
, I've received my BrainChipAkida AKD1000 PCIe neuromorphic chip, which I purchased to adapt SNN and GNN models for use in my Robotic Arm and Autonomous Drone projects. 
joint venture between ST Engineering, DSO National Laboratories and NTU (Nanyang Technological University).
