Wendy Vinson

CommandCore is a new remote drone control station aimed at professionals in the defence, public safety and utilities industries. Customers can choose from an extensive range of Getac devices, accessories and payloads to suit their unique needs and use cases. CommandCore is designed to integrate smoothly within diverse drone ecosystems and support interoperability with third-party technologies.

Getac Technology Corporation (Getac) is a leading provider of rugged computing and mobile video solutions and a manufacturer with advanced in-house capabilities.

CommandCore lets customers combine Getac’s proven rugged hardware with industry-specific accessories, payloads and software based on their individual needs and use case. The result is a comprehensive UAV/USV/UGV control solution that is fully adaptable and can be tailored to suit a wide range of challenging environments and operational scenarios, such as those found in the defence, public safety and utilities industries.

At the heart of CommandCore is Getac’s Ground Control Station (GCS), which serves as a centralised, mission-ready hub, designed for real-time data processing, seamless drone control in extreme conditions, and overall management of operational parameters. Customers can choose between a number of different Getac devices depending on their GCS needs, including the ZX80 fully rugged Android tablet, UX10 fully rugged Windows tablet, S510 rugged Windows laptop, B360 fully rugged Windows laptop, and X600 fully rugged Windows workstation.

The form factor of the GCS can also be customised for maximum flexibility. Options include a fully integrated design with built-in drone controls and an accessory-type design that lets users separate the Getac device from externally housed drone controls as needed. Additional form factors include an office dock design, and a comprehensive suitcase design with dual display for use as part of mobile command and control activity.

Rugged reliability meets modular architecture

CommandCore has been engineered from the ground up to withstand harsh, unpredictable, and mission-critical operational environments without compromising on performance. Its proven rugged design ensures reliability in the field, while its scalable and configurable system architecture enables seamless customisation, expansion, and adaptation to evolving operational requirements across a range of industries.

For the defence sector, this modularity is underpinned by Getac’s sophisticated engineering depth and system-level design skills. Rather than offering a fixed configuration, Getac reserves maximum flexibility within the CommandCore architecture to customise the solution for specific mission requirements and unique end-user needs. By leveraging Getac’s advanced in-house manufacturing capabilities, Getac offers tailored configurations to support the complex demands of multi-domain operations.

An open ecosystem supporting smooth integration

CommandCore integrates smoothly with diverse drone ecosystems, supporting interoperability with third-party technologies and long-term collaborative system evolution. Getac’s GCS is available in both Android and Windows options, while the stable, high performance computing platform supports real-time, mission-critical operations in demanding situations and environments.

Setting a new benchmark in remote drone control solutions

CommandCore facilitates seamless drone operations in extreme terrain and/or high-pressure situations, like those regularly encountered in the defence, public safety and utilities industries:

Defence: Supports intelligence, surveillance and reconnaissance (ISR) activity from both mobile and semi-fixed command posts, informing mission planning and execution strategies in real-time across land, sea and air. Given its robust capabilities, CommandCore is uniquely positioned to support the complex requirements of Army aviation and integrated air defense systems, providing operators with the reliable control and data visualization necessary for critical tactical operations.
Public safety: Drone reconnaissance now plays an integral role in disaster response operations ranging from fires and floods to emergency search and rescue. CommandCore lets first responders gather critical information quickly and efficiently, either from a nearby mobile command vehicle or directly from the scene using a handheld GCS.
Utilities: Enables utilities professionals to remotely inspect large-scale infrastructure and assets such as powerlines, substations, pipelines, and railways using waypoint guidance from a centralised operations centre. Beyond large-scale monitoring, CommandCore delivers the reliable remote control needed to rapidly assess hard-to-reach rural areas, minimising the need for crews to physically navigate high-voltage or dangerous terrain.

Contact Solsta Embedded for more information.

We are delighted to announce that Leander Rabe has been appointed to the position of General Manager, Solsta USA.

Since joining Solsta in November 2025, Leander has demonstrated strong leadership capability and has made a positive impact on both the organisation and the wider team. In his previous role as Vice President of Distribution and Sales, Leander achieved significant progress in strengthening customer relationships and driving growth opportunities across the USA distribution business.

As General Manager, Leander will focus on aligning all aspects of the USA distribution team and systems to better position the business for future growth.  We wish Leander every success in this well deserved new role!

The flexible and reliable TFT LCD display range from Raystar continues to benefit from product innovation with two exciting new products.

2.4-inch 240×320 Transflective TFT LCD Module

The RFN02400A2A0GWDNN00 is a 2.4-inch 240×320 TN Transflective TFT LCD Module designed to deliver excellent visibility in both indoor and outdoor lighting conditions. Featuring the ST7789 IC, it supports MCU, SPI, and RGB interfaces for flexible system integration. Its transflective design offers strong readability while maintaining low power consumption.

Built for long-term reliability, the RFN02400A2A0GWDNN00 operates within a wide temperature range of -20°C to +70°C and offers a 50,000-hour LED backlight lifetime. With a typical supply voltage of 3.3V, this display ensures stable and consistent performance for industrial controls, handheld GPS devices, and outdoor measuring instruments.

8.88-inch 480×1920 MIPI IPS Bar Type TFT Module with PCAP+Optical Bonding

The RF8880B-AYW-MNG1 is an 8.8-inch 480×1920 MIPI IPS bar type TFT LCD module equipped with a Projected Capacitive Touch Panel (PCAP) using optical bonding. The MIPI interface ensures high-speed, low-power transmission, and simplifying system integration for a variety of embedded and industrial applications.

Designed for performance, the RF8880B-AYW-MNG1 features an PCAP+Optical Bonding that enhances sunlight readability, improves visual contrast, and provides reliable touch accuracy. Operating with low power consumption (3.3V, 35 mA typ.), it’s an excellent choice for audio-visual systems, industrial controls, smart home devices, and medical instruments.

For more details on these products and the rest of Raystar’s range, please contact Solsta Embedded.

Strip away the buzzwords and ‘AI hardware’ simply means making trade-offs around processing resources, data movement and storage, and energy. You might be working with a microcontroller (MCU) sporting a digital signal processor (DSP) or Convolutional Neural Network (CNN) accelerator blocks consuming milliwatts, an application SoC with a dedicated NPU, a graphics processing unit (GPU) module, or purpose-built inference accelerators. What matters is whether you can feed the most efficient compute engine for your application with enough data within your power budget.

Understanding these trade-offs in concrete terms – multiply-accumulate operations per second, memory bandwidth, thermal dissipation, interface latency – is what separates specification sheets from shippable products. In this article published by Electronic Specifier, Solsta’s Derek Stewart assesses these trade-offs and explores the right questions to ask to ensure project success.

Designing real-world AI hardware at the Edge

by Derek Stewart, Business Development Engineer, Solsta

Everyone is selling AI now – from Cloud platforms to AI PCs and Neural Processing Units (NPUs) on every System on Chip (SoC), whether you want it or not – but embedded engineers don’t ship hype, they ship efficient, targeted working systems. The reality on the factory floor, in vehicles or in remote devices is that AI lives or dies on how well the design utilises multiply-accumulate operations (the building block of any AI accelerator solution), memory and milliwatts, not marketing slides.

The numbers tell a quieter, more pragmatic story than the vendor presentations suggest. According to IDC, global Edge computing spend is forecast to reach around $378 billion by 2028, whilst IoT Analytics reports that the installed base of connected IoT devices is expected to climb to 21.1 billion in 2025.

This isn’t a science experiment anymore. Edge AI and embedded intelligence are large, fast-growing markets. The question for engineers isn’t if AI belongs at the Edge, but where and how much.

From AI hype to embedded reality

Strip away the buzzwords and ‘AI hardware’ simply means making trade-offs around processing resources, data movement and storage, and energy. You might be working with a microcontroller (MCU) sporting a digital signal processor (DSP) or Convolutional Neural Network (CNN) accelerator blocks consuming milliwatts, an application SoC with a dedicated NPU, a graphics processing unit (GPU) module, or purpose-built inference accelerators. What matters isn’t the marketing label, it’s whether you can feed the most efficient compute engine for your application with enough data within your power budget.

The benefits of implementing AI on the Edge over sending data to a remote server farm to be processed are clear. Edge AI algorithms execute locally so greatly reduce latency, helping to minimise external data bandwidth requirements and connection costs, as well as meet the growing number of privacy constraints, with resource-constrained hardware as the core assumption. That’s the engineering reality: you’re optimising for constraints, not chasing peak performance.

Performance metrics like Tera-Operations Per Second (TOPS) dominate datasheets, but embedded engineers care far more about TOPS per watt. Even at the lower end, devices like the Silicon Labs PG28 ARM Cortex-M33 MCU, costing just a couple of dollars, now integrate an NPU capable of running AI models eight times faster and at 1/6th of the power of the ARM core itself, demonstrating that embedded intelligence can live in milliwatt-class silicon.

Defining embedded intelligence in hardware terms

For embedded engineers, AI hardware is just another set of trade-offs and bottlenecks to juggle. TOPS on a slide means nothing if you can’t feed the accelerator with enough data or keep it within your power envelope.

The distinction between different hardware approaches matters in practical terms and it helps to think about the dimensionality of your data in your application. One-dimensional AI, which only needs to process a single serial stream of complex data such as audio keyword spotting, vibration analysis for predictive maintenance, or time-series anomaly detection, is less complex and is well suited to microcontrollers with integrated NPU/CNN accelerators operating in the milliwatt range. These are low-cost, always-on, ultra-low-power applications where the signal being processed is a potentially complex waveform changing over time. These applications are more machine learning where the model efficiently identifies the unique patterns in the data, defining its desired outputs.

Video applications introduce a second dimension to the data, greatly increasing the AI processing requirements in applications such as multi-camera vision systems, real-time object detection and visual inspection. These applications require SoCs and SoMs (System on Modules) with dedicated NPUs sitting in the watts range, or even GPU-based edge boxes consuming tens of watts for heavier workloads like AI model creation or dynamic model switching. The compute density and memory bandwidth requirements jump significantly when you’re processing image frames rather than sensor streams.

Understanding these trade-offs in concrete terms – multiply-accumulate operations per second, memory bandwidth, thermal dissipation, interface latency – is what separates specification sheets from shippable products. The hardware exists across a spectrum, and the engineering challenge is matching it to the workload and constraints you actually face.

However, there’s a human dimension too. Not every engineer is keen to hand over decades of domain knowledge to a black-box model. One motor-monitoring specialist put it bluntly: after years building hand-crafted mathematical models of bearing wear and vibration signatures, why would he ‘throw AI at it’ and lose visibility into what the system is actually detecting? That reluctance isn’t Luddism – it’s a legitimate concern about explainability, trust and maintainability. The best embedded AI projects don’t replace engineering judgement; they augment it with tools engineers can understand, validate and trust.

Where AI on the Edge fits (and where it doesn’t)

The art isn’t running everything at the Edge or pushing everything to the Cloud. It’s carving up the problem, so each layer does what it’s best at.

Use the Edge when you must: for latency, bandwidth, privacy, or resilience. Autonomous vehicles, industrial control, and smart healthcare often require millisecond response times and enhanced data security and can’t tolerate Cloud latency or connectivity loss. Sensor suites in autonomous vehicles (cameras, radar, lidar) generate tens of terabytes daily, making Cloud streaming impractical. Processing data close to sensors reduces network traffic and energy whilst keeping sensitive data local for regulatory compliance.

Use the Cloud when you can: for heavy training, fleet-wide retraining, and data aggregation. Hybrid architectures often make the most sense, with lightweight inference at the Edge and periodic model updates handled centrally.

Accuracy, safety and ‘good enough’ intelligence

In embedded and safety-critical systems, ‘good enough’ AI is a very precise phrase. You must know what accuracy, false positives, and false negatives mean in real operational terms – and keep deterministic safety nets in place.

Again, this comes down to the trade-offs you must consider between a larger more accurate model, which can take longer to run and consume more power, and a lighter weight model that may not be as accurate.

Real-world deployment accuracy routinely falls several percentage points below laboratory benchmarks due to noise, occlusion, and domain shift. Quantisation techniques, running models at 8-bit or sub-8-bit precision, can significantly reduce the size and execution time (inference latency) of your model and hence energy consumption whilst keeping accuracy within a few points of full-precision baselines, but only after careful validation.

In safety-critical domains, AI should be integrated as decision support and should not be the only safety mechanism, with continued focus on traceability, explainability and adherence to standards like ISO 26262 or IEC 61508.

Practical hardware and tooling considerations

Don’t pick hardware in a vacuum. Modern Edge AI deployments increasingly rely on heterogeneous compute – that is combinations of central processing unit (CPU), GPU, DSP, and NPU working together, with the scheduler assigning workloads based on efficiency and latency requirements. A dedicated NPU might handle continuous inference tasks to benefit from its efficient parallel processing capabilities, whilst the CPU manages the more general-purpose control logic and the GPU processes occasional heavier vision workloads.

The architectural choice between integrated and add-on accelerators matters too. As mentioned, vendors like Silicon Labs offer microcontrollers and ISM band RF SoCs with built-in AI acceleration suitable for one-dimensional workloads, whilst companies like DeepX provide companion chips and modules that bolt onto existing processors (discretely or via an M.2 plug in card) when you need more inference capability without redesigning your entire platform. Their proprietary ‘Intelligent Quantisation’ (IQ8) techniques help to deliver the performance levels required of modern Edge AI deployments. Each approach has trade-offs: integrated solutions offer tighter power management and lower latency, whilst add-on accelerators provide upgrade paths and flexibility for evolving models.

Intel’s reference blueprint for Core Ultra processors demonstrates heterogeneous compute in practice: NPU and integrated GPU configurations balance multi-stream computer vision and small generative models with significantly better performance per watt than GPU-only setups. However, raw silicon capability is only half the story.

Tooling, software development kits (SDKs) and ecosystem support often matter as much as the chip specification. Can you easily quantise and optimise your models for the target hardware? Is there robust debugging support? What’s the long-term roadmap?

The skills gap is real: traditional embedded teams often lack experience in data management, model generation and optimisation techniques like quantisation and pruning, and efficient inference deployment on constrained devices. A slightly smaller NPU with a great toolchain and strong community support often beats a monster chip with a poor software ecosystem.

Start with questions, not chips

The winning teams aren’t the ones with the biggest NPU; they’re the ones who frame the right problem, own their data, develop or select realistic models and choose hardware and tools that match those constraints efficiently.

Start by asking the right questions: What problem are you solving? What latency can you tolerate? What data do you have – quality and quantity? What’s your power budget? What safety requirements apply? Only then look at hardware options.

The organisational challenge is real. Research shows that whilst AI investment is widespread, very few organisations consider themselves truly advanced in AI maturity. When applied correctly to well-defined problems, the results speak: predictive maintenance has demonstrated up to 40% cost reductions and 30-50% reliability improvements.

De-risk projects by bringing hardware, software, and firmware engineers together early, use pre-trained models as a starting point where they exist and simulate performance on different hardware options before committing to silicon. You don’t need to become a data scientist overnight. A disciplined engineering mindset, understanding constraints, trade-offs and verification, is exactly what’s needed to make embedded intelligence work.

If you start with the right questions about latency, bandwidth, power, safety, and data, the AI hardware choices become clearer. Embedded intelligence isn’t about chasing the biggest model; it’s about building systems you can trust, maintain, and ship.

Derek Stewart, Business Development Engineer, Solsta

VPT, Inc. has announced the VSCPL1210SG, the first point of load DC–DC converter in its award–winning VSC Series of space–grade COTS power supplies. Delivering up to 10 A of output current with peak efficiency up to 95%, the VSCPL1210SG expands VPT’s NewSpace product offering and completes a full power–conversion solution that includes isolated DC–DC converters, point of load converters and EMI filters.

The VSC Series is a commercial off–the–shelf (COTS) product line designed for smaller satellites in low Earth orbits (LEO), and NASA Class D missions where the balance of cost and guaranteed performance is critical. This series is radiation tested to 42 MeV/mg/cm² with no destructive SEE events (SEGR, SEB, SEL) and tested to 40 krad(Si), guaranteed to 30 krad(Si), for TID in accordance with VPT’s in–house radiation hardness assurance (RHA) plan.

Operating from a 4.5 to 13.2 V input, and offering an adjustable output from 0.8 to 5.0 V, the VSCPL1210SG delivers reliable performance across the full temperature range of –55 °C to +105 °C with no power derating. Its proprietary packaging enables dual–side heatsinking with low outgassing, while the surface–mount design supports streamlined reflow soldering and simplified board–level integration.

“We designed the VSCPL1210SG to deliver high efficiency, tight regulation, and simplified board-level integration while maintaining the radiation tolerance required for LEO environments,” said Brandon Witcher, Principal Design Engineer. “The goal was to meet NewSpace performance needs without pushing beyond the cost constraints that define this market.”

VPT will introduce two additional point of load converters later this year to further expand the series, : the VSCPL0510SG with a 10 A output and the VSCPL1220SG with a 20 A output.

The VSC Series complements VPT’s hermetic hybrid SVL Series of rad hard products available on DLA SMDs.

The VSCPL1210SG is manufactured in an ISO9001, J-STD-001, and IPC-A-610 certified facility and is available for order now. Sales are subject to all applicable U.S. export license restrictions and regulations.

Contact us for further information.