NEWS


computationally efficient and low-power local inferencing to filter the sensor data and thus offload the downlink.


The ability to autonomously make decisions in space is at least mission-enhancing, and in some cases mission-enabling. For instance, earth observation satellites are beginning to use AI to detect the presence of clouds in captured visual images. If surface detail is obscured by cloud then the image may be rendered useless, in which case it can be discarded and not consume storage memory or downlink bandwidth.


In security applications, where objects on the earth’s surface need to be identified in real- time, object recognition AI can quickly differentiate between, say, commercial ships and military naval vessels to accelerate response time and eliminate long human-in-the-loop analysis cycles.


Moreover, in spacecraft that are designed to land on planets or asteroids, the communications lag time precludes remote control of the landing operation from earth. On-board AI lets the vehicle detect viable landing sites autonomously in real time. There is also emerging interest in using AI technology to monitor the overall health


of the systems on-board satellites and spacecraft by detecting anomalies in measured parameters such as currents, voltages, temperature, mechanical strain, and vibration. This can allow real-time fault detection and early warning, negating the need for human- in-the-loop analysis cycles which can take days or weeks. Given that a complex modern satellite may have several thousand telemetry channels, AI enables real-time analysis of all channels whereas only a subset of telemetry channels may be available for human analysis on the ground.


Life in space


As “space AI” becomes more pervasive, the industry requires cost-effective solutions for hosting inference workloads. There are various ways of implementing AI inferencing in embedded systems. A commonplace approach is to use dedicated DSP resources that are often integrated in compute devices such as FPGAs, GPUs, TPUs and specialized ASICs. Devices such as AMD Versal AI Core adaptive SoCs with integrated AI engines (AIE) are designed to implement the multiply- accumulate operations required by neural networks much more


efficiently.


However, the challenges of preparing systems for a life in space never go away. Space systems are expensive and once launched cannot be repaired. Hence, quality and reliability assurance are critical. It is also well known that space presents a very harsh radiation environment to microelectronics, and commercial parts can experience sudden destructive radiation effects (single event latch-up effects), as well as the gradual deterioration of performance and leakage current (total ionizing dose effects). The AMD Class B qualification and manufacturing test flow is based on the US Department of Defense MIL- PRF-38535 Class B specification for qualification and testing of monolithic integrated circuits. The qualification has been adapted for the advanced organic packaging required by space-grade products such as the AMD XQR Versal AI Core adaptive SoCs, supplementing the vast amount of quality and reliability information that has already been gathered on these parts in extreme temperature environments. Devices are also characterised for radiation effects with a variety of tests exposing them to protons, heavy ions, and gamma radiation. This


protects the continuity of space systems using these devices and enables the organisations deploying them to reprogramme hardware after deployment and conduct necessary Over the Air (OTA) updates.


Finally, longevity is an issue. Satellite manufacturers need support on products sometimes years after launch, by which time many commercial microelectronic components have reached obsolescence and discontinuation. AMD answers these demands with its team of specialised quality and reliability engineers, adaptive SoCs tested and characterised for radiation effects, and production and support of space-grade components continued for many years after introduction.


Mission acceleration While the capabilities of satellite and spacecraft sensors have been increasing dramatically, downlink bandwidth has not been increasing as quickly. AI is a viable way to reduce the demand for limited downlink bandwidth while, at the same time, allowing much faster, in some cases real-time, decision making using the data acquired by the satellite sensors. It can be implemented efficiently in adaptive SoCs that provide dedicated adaptive AI engines.


APRIL 2024 | ELECTRONICS FOR ENGINEERS 5


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