# Toward Sustainable Mars Exploration: A Perspective on Collaborative Intelligent Systems
## 5 Collaborative Energy-Aware Computing
### Next-Generation Low-Power AI  
The AI paradigm prevalent at the time of this writing is centered on obtaining dramatically impressive results at the expense of grand computational costs and power consumption. Migrating the large-scale computing and power infrastructures required to maintain these data center facilities off-planet would require the establishment of power generation plants or the discovery of sources of high energy on or around Mars. Moreover, frequent communication to terrestrial data centers would be infeasible for real-time missions due to the high data transmission latencies and bandwidth requirements between Mars and Earth. As such, remote, resource-efficient AI algorithms are the only tenable solution for extraterrestrial applications of AI [68]. Applications of such AI algorithms for Mars operations include atmospheric and soil composition analyses, automated detection and decision-making from sensory inputs, pathfinding and exploration, as well as situation- and energy-aware management of resources. Research on methods for reducing the compute and power requirements of ML algorithms is being spearheaded by numerous organizations, including the prominent TinyML community. Advancements have been made in algorithmic quantization, compression, and automated optimization techniques to reduce the processing, memory, and energy footprints of common ML models to operate on a variety of low-power devices, ranging from embedded processors to microcontrollers. Quantization decreases model size through a reduction in the precision of values being stored and computed, while compression prunes away unnecessary model elements or determines an alternative, more compact representation. Automated generation and optimization of ML models aims to use ML to create, configure, and tune models to specific application, device, and resource requirements. Each of these methods aims to squeeze ML algorithms into ever-smaller size and energy form factors with minimal loss in model accuracy and precision; however, they are still bound by the restricted performance capabilities of conventional low-power computing elements, and the inefficiencies of traditional computing and programming paradigms applied to on-device ML. A next generation of brain-inspired computing is required. In modeling the mammalian brain's signaling through event-based, electrical impulses, spiking neural networks operate under a resource footprint that is a marginal fraction of their artificial NN counterparts. Implemented as recurrently connected liquid state machines (LSMs), such networks can perform very low-power learning while attaining state-of-the-art levels of performance and accuracy without biologically implausible backpropagation [69]. As an alternative method of condensing observations into highly compact representations for AI processing, hyperdimensional computing (HDC) makes use of very long vectors-often ten thousand values or more-as concept embeddings. Such dense, information-rich representations can be transmitted, manipulated, and computed extremely efficiently, resulting in far less processing, memory, and storage requirements to achieve results on par with traditional compute paradigms [70]. These next-generation AI algorithms all still require thousands to millions of observational data examples from Mars to be trained properly. Neuromorphic algorithms, such as SNNs and HDCs, are suitable for rapid, low-power, on-device adaptation to previously unseen circumstances; however, they must still have a baseline training from Martian data. This need for more data was introduced earlier in Section 3.3. Since ground observations from Mars are, at the time of this writing, sparse and very costly to obtain from the few rovers currently making their way across the Red Planet, we propose synthetic data generation techniques, including generative AI, to supplement our present Martian datasets with generated, virtual samples that are based on known examples.