Embedded IoT system development is crucial for enabling seamless connectivity and functionality across a wide range of applications. However, such a complex process requires cross-domain knowledge of hardware and software and hence often necessitates direct developer involvement, making it labor-intensive, time-consuming, and error-prone. To address this challenge, this paper introduces EmbedGenius, the first fully automated software development platform for general-purpose embedded IoT systems. The key idea is to leverage the reasoning ability of Large Language Models (LLMs) and embedded system expertise to automate the hardware-in-the-loop development process. The main methods include a component-aware library resolution method for addressing hardware dependencies, a library knowledge generation method that injects utility domain knowledge into LLMs, and an auto-programming method that ensures successful deployment. We evaluate EmbedGenius's performance across 71 modules and four mainstream embedded development platforms with over 350 IoT tasks. Experimental results show that EmbedGenius can generate codes with an accuracy of 95.7% and complete tasks with a success rate of 86.5%, surpassing human-in-the-loop baselines by 15.6%--37.7% and 25.5%--53.4%, respectively. We also show EmbedGenius's potential through case studies in environmental monitoring and remote control systems development.

An ongoing dearth of tech talent and an increasing number of business technologists are driving an increase in low-code development technologies, which are projected to total $26.9 billion USD worldwide in 2023, an increase of nearly 20% from 2022, according to a recent forecast from Gartner. Business technologists work outside of IT and create tech or analytics capabilities for internal or external business use. Low-code application platforms are projected to be the largest component of the low-code development technology market, growing 25% to reach nearly $10 billion USD in 2023. Gartner predicts that by 2026, developers outside formal IT departments will account for at least 80% of the user base for low-code development tools, which is up from 60% in 2021. Other key drivers that will accelerate the adoption of low-code technologies through 2026 include an increasing number of enterprise-wide hyperautomation and composable business initiatives, the firm said.

Low code development is getting a lot of traction. Low code platform development provides a more accessible (and typically a graphical) interface for developing applications. Low-code development platforms are philosophically similar to the rapid application development tools of the 1990s and early 2000s. Low code development has many benefits, including possible reduction of development time. I believe low-code AI development platforms could be particularly suitable for SMEs.

Many of our everyday routines and schedules are now handled automatically by robots, digital assistants, and tools as technology advances. These tools aid in the simplification of job procedures and enable us to accomplish a great deal in a short period of time. Adoption of AI in various sectors of the economy has significantly aided societal growth and development. We are all aware of the traditional approach to building AI models, which entails several hours of coding and the requirement for systems with significant computing power. We now have AI tools that allow us to create AI models without writing a single line of code.

Edge Impulse and Infineon have announced cross-platform support for their software environments, allowing for high-powered, flexible machine learning development on the Infineon PSoC 6 microcontroller series. The collaboration gives Edge Impulse studio users access to ModusToolbox, Infineon's MCU configuration software, allowing them to natively develop and configure applications on the PSoC-6 -based CY8CKIT-062S2 Pioneer Kit coupled with the CY8CKIT-028-SENSE Dev Kit, which incorporates accelerometer, gyroscope, magnetometer, microphone, pressure, and temperature sensors. Data from these sensors can now be used with Edge Impulse for the easy generation of TinyML-based AI models, optimized for low-power, private, low-cloud-cost edge environments. These models can then be deployed on any PSoC 6-based MCU. Edge Impulse, the leading development platform for ML on edge devices, allows developers to quickly and easily create and optimize solutions with real-world data.

Traditional computer programming has a steep learning curve that requires learning a programming language, for example C/C, Java or Python, just to build a simple application such as a calculator or Tic-tac-toe game. Programming also requires substantial debugging skills, which easily frustrates new learners. The study time, effort and experience needed often stop nonprogrammers from making software from scratch. No-code is a way to program websites, mobile apps and games without using codes or scripts, or sets of commands. People readily learn from visual cues, which led to the development of "what you see is what you get" (WYSIWYG) document and multimedia editors as early as the 1970s.

This article originally appeared on ElectronicProducts.com The internet of things is a system that connects objects and users via the internet. "Things" are made much simpler using microelectromechanical (MEMS) technology. MEMS devices come in a variety of flavors, including magnetometers, microphones, oscillators, sensors, and switches. MEMS can also improve traditional technologies used in IoT applications.

AI deployment in the enterprise skyrocketed as the pandemic accelerated organizations' digital transformation plans. Eighty-six percent of decision-makers told PricewaterhouseCoopers in a recent survey that AI is becoming a "mainstream technology" at their organization. A separate report by The AI Journal finds that most executives anticipate that AI will make business processes more efficient and help to create new business models and products. The emergence of "no-code" AI development platforms is fueling adoption in part. Designed to abstract away the programming typically required to create AI systems, no-code tools enable non-experts to develop machine learning models that can be used to predict inventory demand or extract text from business documents, for example.

Low-code is a software development method that enables for rapid app delivery while requiring little to no coding. As a result, low-code platforms are a group of software tools that enable the visual development of apps using intuitive modeling and a graphical user interface (GUI). Low-code reduces or eliminates the need for coding, speeding up the process of bringing apps to market. What is a low code platform? Low-code development platforms are based on the concepts of model-driven design, automatic code generation, and visual programming.

Synaptics Incorporated announced a partnership with Edge Impulse, the leading development platform for machine learning on edge devices. The partnership combines Synaptics' Katana Ultra Low-Power Edge AI Platform with the Edge Impulse software development platform used by thousands of embedded developers to create, train and deploy custom models for a wide range of AI applications. The Edge Impulse Embedded ML Platform allows developers to create production-ready models, faster and efficiently. Additionally, it enables easy testing, training, and model optimization in a complete MLOps environment. For example, developers can create new tinyML models in as little as five minutes using an innovative and interactive cloud-based platform without writing a single line of code.