Hardware Development Trends

THIS JOINT COULD PROPEL ROBOTICS FORWARD – AND NO ONE IS USING IT❓ Three years ago, Japanese researchers Kazuki Abe, Kenjiro Tadakuma, and Riichiro Tadakuma introduced the ABENICS 3D joint. An active ball joint with three rotational degrees of freedom, achieved through a precise combination of bevel and specially shaped gears. It moves like a human shoulder or hip – no slip, no sensors, high torque, and impressive positional accuracy. As an engineer, I see this as a dream component for humanoid robots, especially in the shoulders, where space is limited and mobility is critical. And yet: no production model, no prototype at trade shows, no visible implementation in industry. Why does something like this remain in the lab? Is it a lack of courage to replace existing solutions? Manufacturing challenges? Or have we as an industry become so comfortable with “good enough” that we stop implementing real breakthroughs? My view: Any technology that delivers precision, strength, and compactness in a single unit deserves to make the leap into real-world applications – otherwise, we lose years of progress. What do you think: Is the problem the technology, the market, or us engineers? Follow me if you want to see the technologies that deserve to break out of the lab – and share your perspective in the comments. Best Regards #CobotUli ULMO Consulting & Marketing #Robotics #Innovation #Engineering I post at 8:00, 11:30, and 17:00 (Berlin time) 3x daily, 100% real robotics. No fluff. No filters. No fakes. PS: LinkedIn hides most posts. 👉 Join my group or miss out: https://lnkd.in/e9skpAF

AI is not powered by one chip. Different workloads need different architectures. Training a model, running inference, handling edge devices, or generating tokens all demand different strengths. That is why understanding compute matters more than ever. Here are 5 AI compute architectures explained 👇 𝗖𝗣𝗨 Best for general-purpose computing, control logic, sequential tasks, and low-latency operations across diverse workloads. 𝗚𝗣𝗨 Built for parallel processing and massive matrix calculations. Ideal for deep learning training and high-throughput AI workloads. 𝗧𝗣𝗨 Specialized for tensor operations and large-scale model training. Optimized for efficient AI acceleration at scale. 𝗡𝗣𝗨 Designed for energy-efficient on-device inference. Common in smartphones, laptops, cameras, and embedded systems. 𝗟𝗣𝗨 Focused on fast LLM inference with deterministic, low-latency token generation for language model execution. What This Means: The future of AI infrastructure is not one winner. It is choosing the right processor for the right job. Smart AI teams optimize workloads across architectures instead of forcing every task onto the same hardware. Which architecture do you think will matter most over the next 3 years?

Today, Science Robotics has published our work on the first drone performing fully #neuromorphic vision and control for autonomous flight! 🥳 Deep neural networks have led to amazing progress in Artificial Intelligence and promise to be a game-changer as well for autonomous robots 🤖. A major challenge is that the computing hardware for running deep neural networks can still be quite heavy and power consuming. This is particularly problematic for small robots like lightweight drones, for which most deep nets are currently out of reach. A new type of neuromorphic hardware draws inspiration from the efficiency of animal eyes 👁 and brains 🧠. Neuromorphic cameras do not record images at a fixed frame rate, but instead have the pixels track the brightness over time, sending a signal only when the brightness changes. These signals can now be sent to a neuromorphic processor, in which the neurons communicate with each other via binary spikes, simplifying calculations. The resulting asynchronous, sparse sensing and processing promises to be both quick and energy efficient! 🔋 In our article, we investigated how a spiking neural network (#SNN) can be trained and deployed on a neuromorphic processor for perceiving and controlling drone flight 🚁. Specifically, we split the network in two. First, we trained an SNN to transform the signals from a downward looking neuromorphic camera to estimates of the drone’s own motion. This network was trained on data coming from our drone itself, with self-supervised learning. Second, we used an artificial evolution 🦠🐒🚶♂️ to train another SNN for controlling a simulated drone. This network transformed the simulated drone’s motion into motor commands such as the drone’s orientation. We then merged the two SNNs 👩🏻🤝👩🏻 and deployed the resulting network on Intel Labs’ neuromorphic research chip "Loihi". The merged network immediately worked on the drone, successfully bridging the reality gap. Moreover, the results highlight the promises of neuromorphic sensing and processing: The network ran 10-64x faster 🏎💨 than a comparable network on a traditional embedded GPU and used 3x less energy. I want to first congratulate all co-authors at TU Delft | Aerospace Engineering: Federico Paredes Vallés, Jesse Hagenaars, Julien Dupeyroux, Stein Stroobants, and Yingfu Xu 🎉 Moreover, I would like to thank the Intel Labs' Neuromorphic Computing Lab and the Intel Neuromorphic Research Community (#INRC) for their support with Loihi (among others Mike Davies and Yulia Sandamirskaya). Finally, I would like to thank NWO (Dutch Research Council), the Air Force Office of Scientific Research (AFOSR) and Office of Naval Research Global (ONR Global) for funding this project. All relevant links can be found below. Delft University of Technology, Science Magazine #neuromorphic #spiking #SNN #spikingneuralnetworks #drones #AI #robotics #robot #opticalflow #control #realitygap

Don't reduce the carbon footprint of your products without understanding all the possible trade-offs. You could end up increasing your environmental impact instead. Here are 3 things to consider when designing sustainable sound experiences: ⚠ Lowest footprint ≠ Winning concept Successful circular products don’t have the lowest environmental burden by default. Modularity is considered a circular design practice, but it also contributes to increased carbon footprint and depletion of materials (mostly gold, beryllium, and neodymium). A modular product containing electronics has roughly 10% higher impact for both GWP and ADP. 🛠 UX plays a core role as much as CMF and ID Functionalities and usability have their footprint: removing a battery from earpods charging case and using the smartphone battery instead decrease hardware volume and materials footprint (-25%) . The same works for magnets: fashionable to have an earpod snapping to the charging case, until you realize that 1/3 of the overall material impact is due to neodymium. 🔄 Trade-offs are inevitable It is better to design for one core circular principle than having a concept that mediocrely covers all of them. A concept can successfully be repairable and fit a circular ecosystem, but it will hardly be repairable, modular, recyclable, refurbishable, low-carbon, low-resource, long-lasting, energy-efficient, biodegradable, compostable and fit a circular ecosystem. Sustainable design isn’t about ticking every box. It’s about making informed choices that truly minimize impact. ➡What’s your take? Which design principle would you prioritize for a truly circular product? Drop your thoughts below and let’s discuss! #sustainabledesign

China just bent the rules of electronics — literally. Facinating? Chinese and global researchers are advancing Metal-Polymer Conductors (MPCs) — circuits made from liquid metals like gallium–indium embedded in elastic polymers — that defy traditional rigid wiring by remaining conductive even when stretched up to 500% or more. Why this is a big deal: 🔹 High Stretchability: Certain liquid-metal conductors maintain electrical conductivity even when stretched 5× their original length. 🔹 Durability: Printable metal-polymer conductors can withstand over 10,000 cycles of stretching with minimal resistance change (<3%). 🔹 Conductivity: Hybrid conductors based on indium alloys can achieve extremely high conductivity (~2.98 × 10⁶ S/m) with minimal resistance change under extreme strain. 🔹 Fine Feature Sizes: Advanced techniques can pattern circuits as small as 5 micrometers, rivaling conventional PCBs. Market Insight: The global market for wearable and flexible devices is expected to surge into the hundreds of billions of dollars, with advanced stretchable materials at the core of the next wave of innovation. (Wearable tech projected >US$150B by 2026 in soft electronics growth — wearable industry data) Where AI Fits In: AI is not just hype — it’s accelerating how we design and discover materials like MPCs. AI/ML models help predict material properties — like conductivity and mechanical resilience — before physical prototypes are made. Computational simulations can evaluate thousands of polymer + metal combinations far faster than physical testing alone. AI-assisted optimization reduces lab iterations, cutting time and cost in early-stage development. In other words: AI + materials science = faster discovery of smarter, stretchable electronics. Potential Applications: Soft robotics that mimic human motion Wearables that feel like fabric Artificial skin with embedded sensing Health monitoring devices that conform to the body On-skin motion recognition and bioelectronics. The era of electronics you can twist, stretch, and wear is here — and AI is helping make it a reality. #FlexibleElectronics #MaterialsScience #AIinInnovation #SoftRobotics #WearableTech #DeepTech #FutureOfElectronics #Innovation

Operating our data centers more sustainably means being thoughtful about every step - including the materials we use for things like circuit boards and hardware devices. Copper is one of those essential materials, and now there’s a way to source it that supports our goal of The Climate Pledge. Amazon Web Services (AWS) is the first buyer of copper produced from Rio Tinto's innovative Nuton technology. It's a breakthrough process that uses microorganisms - or "bioleaching" - to extract copper from sulfide ores (which are traditionally hard to process and often become waste). Why does that make a difference?   It removes the need for traditional concentrators, smelters, and refineries. The process uses up to 80% less water usage than traditional mining methods. It also has a carbon footprint well below the global average. It significantly shortens the mine-to-market supply chain.   This innovation is another example that solutions exist, and forward momentum continues. Amazon is working across our entire value chain - from steel and concrete to copper - to source materials differently, and I'm thrilled to see AWS leading the industry in the right direction! Learn more about our work on copper in this The Wall Street Journal article by Ryan Dezember: https://lnkd.in/g9AgshDn  

🚀 When light becomes a manufacturing tool at the scale of life We often talk about precision engineering. But what happens when precision reaches the nanometer scale — small enough to interact with the human body? Enter femtosecond lasers. A femtosecond is 10⁻¹⁵ seconds. At this timescale, lasers don’t just cut metal — they reshape it with almost no heat impact. This enables ultra-precise structuring of metals without damaging surrounding material. And this is not just a lab curiosity — it’s already being applied in medical technologies that operate inside blood vessels. 🔬 What does this enable in practice? 1. Vascular stents Femtosecond lasers are used to cut and structure metals like nitinol with extreme precision: Complex mesh geometries for flexibility and strength Smooth, damage-free edges Surface textures that can reduce thrombosis risk 2. Microfluidic implants & drug delivery systems Lasers can engrave microscopic channels into metal and polymer surfaces: Controlled drug release inside the bloodstream Implantable diagnostic systems Lab-on-chip devices operating at micro-scale 3. Surface-functionalized implants Femtosecond lasers can “program” how a surface interacts with biology: Nano-patterns that promote cell adhesion Structures that reduce bacterial growth Textures that influence blood flow and protein interaction 4. Miniaturized surgical tools The same technology enables: Microneedles for minimally invasive treatments Ultra-sharp surgical components Tools designed for navigating extremely small anatomical pathways 💡 The bigger shift We are moving from manufacturing devices to engineering interfaces with living systems. 👉 Not just shaping metal 👉 But controlling how it behaves inside the human body Femtosecond lasers are one of the key technologies making this possible. #DeepTech #MedTech #AdvancedManufacturing #Foresight #Innovation #LaserTechnology #FemtosecondLaser #Photonics #PrecisionEngineering #Microfabrication #Nanotechnology #BiomedicalEngineering #MedicalDevices #HealthTech #Biotech #Implants #Microfluidics #FutureOfHealthcare #NextGenTech #TechInnovation #EngineeringExcellence #Industry40 #DigitalManufacturing

What happens to Google’s hardware when its 'first life' in the data center is over? ⚙️ A decade ago, we began imagining a system that allows our decommissioned servers to get a second life. Today, that vision is a global reality: In 2024 alone, we successfully recovered 8.8 million components from our data centers, including over 3 million hard drives. Through reusing, repairing, or recycling hardware, we can reduce material costs and associated carbon emissions for data centers. We've learned a lot along the way, and we're proud to share our insights in a new report. Check out our "Bridging the Gap" analysis and share it with colleagues who are working to advance operational circularity: goo.gle/3O8dlIG

Quick sustainability win of the week: Start tracking peripheral purchases. You’d be amazed how few organisations do this! We've just wrapped up a review across five large orgs (each with 25,000+ employees). Every single one had the same approach with new starters: onboarding kits were given by default, including a keyboard, power blocks, mouse, headset, docking station, cables, bag, plus sometimes even phone cases. And in every case, 50 to 70% of that kit went unused. Straight into drawers, or binned after a year and straight to landfill. Often because the gear was cheap or the user already had better. There was nearly always also a constant churn of replacement accessories being ordered via internal "shops" with very little oversight. New chargers, random adapters, yet another headset. One organisation was spending over $5 million a year on peripherals alone. That’s $5 million in Scope 3 emissions and plastic waste that is totally invisible, unmanaged, and unnoticed. This isn't procurements fault, they are only following a plan, it’s actually more of a cultural and process issue. TBH, if we’re actually serious about doing something positive with sustainability, this kind of waste has to go. I'd personally recommend a simple approach like: 1) Ditch the onboarding kits, just ask what people actually need. 2) Track peripherals separately from core assets. 3) Introduce a reuse-before-rebuy policy (refurb stuff is awesome). 4) Audit what’s in stock before raising a new PO. Small fix. Big impact. Less plastic, less carbon, less water usage, more $$$$ saved. 😃