small8smart.ai

#Small & Smart AI | AI powered Small & Smart Edge

#Edge AI

#Continuously operating, interacting with and learning from its environment on its own and in real time

#AI Accelerator | AI Processing Unit (AIPU) | Digital in-memory (D-IMC) technology | SRAM (Static Random-Access Memory) memory densely interleaved with digital computation, each memory cell effectively becoming matrix-vector compute element | Increasing the number of operations per computer cycle (one multiplication and one accumulation per cycle per memory cell | 214 Tera-Operations per Second (TOPS) of AI processing | Power efficiency of 50 TOPS per Watt at 50% input and data sparsity | Metis AIPU run ResNet50v1 neural network processing 3,200 frames per second, with a relative accuracy of 99.9% | High performance AI acceleration at the edge | Energy efficient AI processing unit | Proprietary digital in-memory computing and RISC-V technology | Architecture minimizes data movement between memory and compute elements | Metis AI Platform

#Ability to change resource usage over time

#Off road Autonomy

#Physical AI

#Autonomous Inspection

#Robotic AI

#Robotic Autonomy

#Uncertainty aware AI

#Handling tasks without human intervention

#Spontaneously learning and improving from experiences

#Delivery robot

#Cancer Detection with Molecular MRI | Superparamagnetic Iron Oxide Nanoparticles | Bio-safe magnetic particles are attracted to tumor and detected | Patients are given a low-dose injection of MagSense imaging agent nanoparticles | Nanoparticles find and bind to tumor cells | Tumor binding makes nanoparticles superparamagnetism detectable for MRI imaging | Nanoparticles are safely cleared by liver where iron core is metabolized to ferritin in hemoglobin production pathway | No ionizing radiation | No radioactive tracers | No strong magnetic fields | Nanoparticles are designed to be detectable and differentiated image contrast for molecular MRI

#Precision application

#Manual use switch

#Targeted Efficiency

#Optimizing resources

#Reducing operating cost

#Sustainability

#Smart Insights

#Smart Decisions

#Autonomous technology

#Autonomous Platform

#Reducing labor costs

#Reducing maintenance costs

#Autonomous system

#Remotely monitoring

#Autonomous tractor

#Equipment availability

#Equipment runtime

#Dynamic sensing

#Bringing sensors to assets

#Dispatching agile mobile robots equipped with sensors to collect data on site

#More accurate data models

#Virtual Wall Functionality

#Automatic Movement Control

#Camera operating as an Edge Device

#Compressed video stream (H264)

#RTSP protocol

#MJPEG stream

#Live image with AI overlay

#Intelligent camera

#Digital Manufacturing

#Autonomous routes

#Digital Twin

#Points of Interest (POI)

#Augmented reality

#3D camera

#Z-accuracy

#Stereo camera

#Field of view

#RGB sensor

#AI-in-a-box

#Neuromorphic processor

#Sustainable AI technology

#Low-shot learning

#Biometric recognition

#Image classification model

#Temporal Event Neural Network

#Edge AI model

#Intelligent sensor technology

#Natural hydrogen: < $1/kg

#White hydrogen: carbon intensity with 75% hydrogen and 22% methane, rises to 1.5 kg CO2e per kg H2

#Gold hydrogen: highest production tax credits (PTC) because the lifecycle carbon intensity below 4 kg CO2e per kg H2

#Grey hydrogen: produced from fossil fuels, costs less than $2 per kilogram (kg) of hydrogen on average

#Green hydrogen: > $6/kg

#Machine learning (ML) to reliably distinguish between a significant and insignificant event on the power grid

#Generative physical AI

#Robotics stack

#Humanoid foundation model

#Autonomous machine development

#Cognitive AI-driven capabilities

#Robot-agnostic software platform

#Robotic-grasping

#Synthetic data generation

#Autonomous mobile robot

#Digital twin technology

#Reference workflow

#Training robots in virtual environment

#Robotic arm

#Robot work cell

#IoT data

#Digital twin workflow

#AI-enabled autonomous machine

#Software frameworks and robot model

#Human coworker

#Intelligent assistant

#Edge AI solution

#Robotics skills

#Electric Vehicle (EV) charging

#Open Smart Charging Protocol (OSCP)

#Cell-to-pack (CTP) design: integrating battery cells directly into battery pack

#Cell-to-chassis (CTC) design: incorporating battery cells directly into vehicle chassis

#Skateboard platforms pre-equipped for self-driving capabilities

#10-minute EV charging benchmark

#Perceptual AI hardware

#Perceptual AI software

#Perceptual AI algorithm

#Neural network architecture

#AI vision system

#Device-agnostic AI system

#Transformers: class of neural network models originally designed for natural language processing

#Polynomial-based continuous convolutions

#Neuromorphic sensing and computing

#Spiking neural network algorithms

#AI Processing Unit (AIPU)

#Vision AI in (biometric) access control

#8-bit integer inference arithmetic vs 32-bit floating-point full-precision

#False positives

#False negatives

#Temporal Event Neural Networks (TENNs)

#Capturing thermal, acoustic, and visual data consistently

#Cardiac ablation: medical procedure used to treat irregular heart rhythms (arrhythmias) by creating small scars in heart tissue

#Atrial flutter: abnormal heart rhythm

#Fluoroscopy: medical imaging technique using X-rays to create real-time moving images of internal structures such as heart

#Active MR tracking: real-time localization of catheters during MRI-guided interventions, utilizing microcoils or antennas to provide precise positional information, enhancing visualization within MR images, reducing the need for manual adjustments and improving efficiency compared to passive methods, integrates tracking sequences with imaging, allowing for dynamic updates of the imaging plane as the device moves

#Microcoils: enhancing MR tracking accuracy by providing improved sensitivity and localization of devices within MRI environment, enabling high frame rates, allowing real time tracking of moving instruments

#Micro transmit tracking

#Centroid pixel method

#Phase field Dithering

#Active microcoils

#Automatic registration of tracked devices

#IO-Link: an open-standard communication protocol (IEC 61131-9) designed for connecting sensors and actuators in industrial automation

#Detecting anomalies

#Active stereo vision

#Time Of Flght (TOF)

#3D depth sensing

#Smart home device

#Miltimodal

#Perceptual mode

#1550nm LiDAR | Advantages: safety, range, and performance in various environmental conditions | Enhanced Eye Safety: absorbed more efficiently by cornea and lens of eye, preventing light from reaching sensitive retina | Longer Detection Range | Improved Performance in Adverse Weather Conditions such as as fog, rain, or dust | Reduced Interference from Sunlight and Other Light Sources | More expensive due to complexity and lower production volumes of their components

#SLAM | Simultaneous Localization and Mapping

#Building Information Modeling (BIM)

#Architecture, Engineering, and Construction (AEC)

#3D modeling

#4D modeling (time scheduling)

#5D modeling (cost estimation)

#SLice Integration by Vision Transformer (SLIViT)

#Retinal scan

#Ultrasound video

#CT

#MRI

#Disease-risk biomarker

#Pre-training method

#Fine-tuning method

#Disease trajectory

#Tailoring treatment

#Fine-tuning 2D model on 3D scans

#Downstream learning

#Deep-learning framework

#Agentic workflow

#Vector database

#Learning Management System (LMS)

#Time To First Token (TTFT)

#Multimodal AI

#Robotic embodiment

#Humanoid robot

#Universal Scene Description (OpenUSD)

#Cognitive robotics

#Vertical movement

#Actuated linear guide system

#Actuator

#Field Foundation Models (FFM) | Physical system intelligence as the first risk-aware model for robots | Enabling any embodiment to autonomously operate in highly dynamic environments without GPS, pre-defined maps, or pre-programmed routes | Allowing robots to be deployed at scale and without human intervention for nearly any application

#Deployment of neural networks near sensors | Low-power FPGAs | Edge implementation of models

#Office of United States Trade Presentative (USTR) | Tariffs | Angola: 15% | Bangladesh: 20% | Bosnia and Herzegovina: 30% | Botswana: 15% | Brunei: 25% | Cambodia: 19% | Cameroon: 15% | Chad: 15% | Democratic Republic of the Congo: 15% | Equatorial Guinea: 15% | European Union: 15% | (for most goods)Falkland Islands: 10% | Fiji: 15% | Guyana: 15% | India: 25% | Indonesia: 19% | Iraq: 35% | Israel: 15% | Japan: 15% | Jordan: 15% | Kazakhstan: 25% | Laos: 40% | Lesotho: 15% | Libya: 30% | Leichtenstein: 15% | Madagascar: 15% | Malawi: 15% | Malaysia: 19% | Mauritius: 15% | Moldova: 25% | Mozambique: 15% | Myanmar: 40% | Namibia: 15% | Nauru: 15% | Nigeria: 15% | North Macedonia: 15% | Pakistan: 19% | Philippines: 19% | Serbia: 35% | South Korea: 15% | Sri Lanka: 20% | Switzerland: 39% | Taiwan: 20% | Thailand: 19% | Tunisia: 25% | Vanuatu: 15% | Vietnam: 20% | Zambia: 15% | Zimbabwe: 15%

#Parameters | Weights | Internal components of a model that guide its behavior

#Large Language Model (LLM) | Foundational LLM: ex Wikipedia in all its languages fed to LLM one word at a time | LLM is trained to predict the next word most likely to appear in that context | LLM intellugence is based on its ability to predict what comes next in a sentence | LLMs are amazing artifacts, containing a model of all of language, on a scale no human could conceive or visualize | LLMs do not apply any value to information, or truthfulness of sentences and paragraphs they have learned to produce | LLMs are powerful pattern-matching machines but lack human-like understanding, common sense, or ethical reasoning | LLMs produce merely a statistically probable sequence of words based on their training | LLMs are very good at summarizing | Inappropriate use of LLMs as search engines has produced lots of unhappy results | LLM output follows path of most likely words and assembles them into sentences | Pathological liars as a source for information | Incredibly good at turning pre-existing information into words | Give them facts and let them explain or impart them

#Retrieval Augmented Generation. (RAG LLM) | Designed for answering queries in a specific subject, for example, how to operate a particular appliance, tool, or type of machinery | LLM takes as much textual information about subject, user manuals and then pre-process it into small chunks containing few specific facts | When user asks question, software system identifies chunk of text which is most likely to contain answer | Question and answer are then fed to LLM, which generates human-language answer in response to query | Enforcing factualness on LLMs

#Ethernet Cameras | Ethernet Vision

#Smart electric vehicle technology | XPENG | AI-driven mobility company | Designs, develops, manufactures, and markets Smart EVs | Catering to tech-savvy consumers | Develops Full-stack advanced driver-assistance system (ADAS) technology | Intelligent in-car operating system | Xmart OS: from driving cockpit to intelligent space | XPILOT ASSIST: Intelligent driving assistance-Easy to drive, easy to park | Over the air software update (OTA) | AI-powered production car equipped with an L3-grade computing platform | Effective computing power exceeding 2000 TOPS | Onboard deployment of VLA (Vision-Language Action) + VLM (Vision-Language Motion) models | Autonomous driving research | Large-scale fleets | Vast real-world data | Data-driven era |

#Vision-language model (VLM) | Training vision models when labeled data unavailable | Techniques enabling robots to determine appropriate actions in novel situations | LLMs used as visual reasoning coordinators | Using multiple task-specific models

#AI models deployed in embedded systems at edge | Brushless DC motors | Hall effect sensors | Optical encoders | Sensorless motor control | Field-oriented control | Artificial intelligence at edge | Three fundamental modalities: vision, sound, and motion | Using AI models to infer information about device environment | Linear algorithms | Software and hardware combination | Deploying multiple AI models in embedded devices requires edge processors designed to run AI | Embedded systems using AI can be considered open | Sensor fusion utilizes combined data from multiple sensors | AI-based vision systems are more adaptable to natural variations inherent in object inspection | Objects can be identified and inspected more quickly with greater flexibility | Strong multimodal AI, a single model will process multiple types of data | Control algorithms will use inputs generated by AI, inferred from multiple sources of data | AI inferencing in data flow | AI-enabled image sensors are perfect for gesture detection | Event detection based on sound is an active area of development | On device learning in real time

#Robot autonomy system combining the benefits of Visual SLAM positioning with advanced AI local perception and navigation tech | Visual Al technology | AI-based autonomy solutions | Visual SLAM | Dynamic obstacle avoidance | Constructing accurate 3D maps of the environment using sensors built into robots | Algorithms precisely localize robot by matching what it observes at any given time with 3D map | Using AI driven perception system robot learns what is around it and predicts people actions to react accordingly | Intelligent path planning makes robot move around static and dynamic obstacles to avoid unnecessary stops | Collaborating with each others robots share important information like their position and changes in mapped environment | Running indoors, outdoors, over ramps and on multiple levels without auxiliary systems | Repeatability of 4mm guarantees precise docking | Updates the map and shares it with the entire fleet | Edge AI: All intelligence is on the vehicle, eliminating any issue related to the loss of connectivity | VDA 5050 standardized interface for AGV communication | Alphasense Autonomy Evaluation Kit | Autonomous mobile robot (AMR) | Hybrid fleets: manual and autonomous systems work collaboratively | Equipping both autonomous and manually operated vehicles with advanced Visual SLAM and AI-powered perception | Workers and AMRs share the same map of the warehouse, with live position data of each of the vehicles | Turning every movement in warehouse into shared spatial awareness that serves operators, machines, and managers alike | Equiping AGVs and other types of wheeled vehicles with multi-camera, industrial-grade Visual SLAM, providing accurate 3D positioning | Combining Visual SLAM with AI-driven 3D perception and navigation | Extending visibility to manually operated vehicles, such as forklifts, tuggers, and other types of industrial trucks | Unifying spatial awareness across fleets | Unlocking operational visibility | Ensuring every movement generates usable data | Providing foundation for smarter, data-driven decision-making | Merging manual and autonomous workflows into a single connected ecosystem | Real-time vehicle tracking | Traffic heatmaps | Spaghetti diagrams | Predictive flow analytics | Redesigning layouts | Optimizing pick paths | Streamlining material handling | Accurate vehicle tracking | Safe-speed enforcement | Pedestrian proximity alerts | Lowerung insurance claims | Ensuring regulatory compliance | Making equipment smarter, scalable, interoperable, and differentiable | Predictive maintenance | Fleet optimization | Visual AI Ecosystem connecting machines, people, processes, and data | Autonomous robotic floor cleaning | Industry 5.0 by adding people-centric approach | Visual AI to providing real-time, people-centric decision-making capabilities as part of autonomous navigation solutions | Collaborative Navigation transforming Autonomous Mobile Robots (AMRs) into mobile cobots | Visual AI confering robots the ability to understand the context of the environment, distinguishing between unobstructed and obstructed paths, categorizing the types of obstacles they encounter, and adapting their behavior dynamically in real-time | Automatically generating complete and very accurate 3D digital twin of an elevator shaft | Autonomous eTrolleys tackling last-mile problem |Autonomous product delivery at airports

#AI-enabled Microcontrollers Expanding Edge AI Opportunities | AI accelerators and related resources included in modern microcontrollers, in combination with technology developments and toolset enhancements that shrink the size of deep learning models, are making it possible to run computer vision, speech interfaces, and other AI capabilities at the edge

#Metal-organic framework (MOF) | Extremely small structures made from metal and organic molecules | Fillled with porous cavities | Gram of MOF material can have the surface area of a soccer field | Atoco MOFs are made of elements designed to adsorb specific molecules from atmosphere, such as H2O or CO2 | A typical data center consumes nearly 530,000 gallons wster a day

#Reticular Materials | Highly ordered, porous structures | Formed by linking molecular building blocks with strong bonds | Customizable frameworks and high porosity enable precise control over physical, chemical, and mechanical properties | Internal surface areas comparable to a football field per gram | Reticular materials such as Metal–Organic Frameworks (MOFs) and Covalent Organic Frameworks (COFs) excel in adsorption, separation, and energy storage | Strong bonds provide stability, allowing them to endure extreme temperatures and harsh environments | Ideal for carbon capture and atmospheric water harvesting | Led by Atoco founder and inventor of reticular chemistry, Prof. Omar Yaghi, Atoco scientists have developed a portfolio of MOFs and COFs demonstrating unmatched carbon capture and atmospheric water harvesting performance

#Rare earths | Chinese heavy and magnet export restrictions | Essential to wind power, EVs, aircons, drones and, critically, defence tech | Export controls issued by China restrict the export of seven heavy rare earths i Department of Defense and Apple invested in MP Materials | New paradigm: government support to build ex-China supply chain of magnet metals like neodymium, praseodymium, terbium, dysprosium and samarium | The latest export controls from China impact not just supply of rare earth metals and magnets, but also technology and expertise to refine and produce value-added products | China can limit exports of magnets containing any portion of Chinese materials or tech | Companies with any affiliation to foreign militaries will be largely denied export licenses | Any requests to use rare earths for military purposes will be automatically rejected | China will also be able to use its discretion to halt exports of rare earth materials intended to be used in semiconductors and memory chips | Holmium | Erbium | Thulium | Europium | Ytterbium | Samarium | Terbium

#Critical minerals in Artificial Intelligence | At the core of AI transformation lies a complex ecosystem of critical minerals, each playing a distinct role | Boron: used to alter electrical properties of silicon | Silicon: fundamental material used in most semiconductors and integrated circuits | Phosphorus: helps establish the alternating p-n junctions necessary for creating transistors and integrated circuits | Cobalt: used in metallisation processes of semiconductor manufacturing | Copper: primary conductor in integrated circuits | Gallium: used in compound semiconductors such as gallium arsenide (GaAs) and gallium nitride (GaN) | Germanium: used in high-speed integrated circuits and fibre-optic technologies | Arsenic: employed as a dopant in silicon-based semiconductors | Indium phosphide: widely used in optical communications | Palladium: used in production of multi-layer ceramic capacitors (MLCCs) | Silver: the most conductive metal used in specialised integrated circuits and circuit boards | Tungsten: serves as a key material in transistors and as a contact metal in chip interconnects | Gold: used in bonding wires, connectors, and contact pads in chip packaging | Europium: enables improved performance in lasers, LEDs, and high-frequency electronics essential to AI systems and optical networks | Yttrium: improves the efficiency and stability of materials like GaN and InP, supporting advanced applications in photonics, high-speed computing, and communications technologies

#Critical minerals in Data Storage for Artificial Intelligence | Lithium: powers batteries that support portable storage devices, SSDs, and memory-rich electronics, in data centres, lithium-ion battery arrays ensure continuous power to storage systems | Silicon: the heart of solid-state drives (SSDs) and memory chips | Manganese: next-generation memory technologies, such as resistive RAM (ReRAM) and spintronic memory, also used in lithium-ion batteries for backup power in enterprise storage systems | Praseodymium: helps improve the efficiency and durability of motors in HDDs | Neodymium: key component in neodymium-iron-boron (NdFeB) permanent magnets used in spindle motors of hard disk drives (HDDs).| Samarium: used in samarium-cobalt (SmCo) magnets, which offer exceptional thermal stability, these magnets are favoured for mission-critical and defence-grade storage systems | Gadolinium: used in advanced memory storage systems. It plays a role in magneto-optical storage technologies | Dysprosium: important for data storage applications | Platinum: used in the manufacture of high-purity glass and crucibles required in the production of memory and storage devices | Gold: used in memory chips

#Critical minerals for Optics, Imaging & Advanced Materials | Graphite: high-speed electronics, advanced sensors, and thermal management systems | Copper: short-distance data transmission in AI data centres | Germanium: a key material in thermal imaging, night-vision optics, and fibre-optic communication systems | Indium: optical communication systems | Praseodymium: specific types of lasers and optical materials | Neodymium:solid-state lasers | Holmium: specialised laser systems, particularly medical and scientific applications

#Critical minerals for Power Supply & Batteries | Lithium: portable electronics, wearables, electric vehicles | Graphite: stores lithium ions during charging process and releases them during discharge | Manganese: used in various lithium-ion battery chemistries | Cobalt: critical to the performance of premium mobile and computing devices | Nickel: crucial for electric vehicles, high-performance electronics, and energy-intensive AI systems