Alif Semiconductor’s Post

Want to experiment with AI but unsure where to start? Alif Semiconductor's AI/ML evaluation kit comes pre-loaded with machine learning models, allowing you to start experimenting right away, even if you have no prior experience. In the step by step guide below, we will walk you through the bring-up of our AI/ML AppKit and help you get your first ML models up and running. Alif will also set you up with one of our dedicated AI/ML partners, who can help you build and deploy custom models tailored to your specific needs, whether it's image recognition, predictive analytics, or something else entirely. Our 32-bit microcontrollers with neural processing units, leveraging the Arm Ethos-U55 NPU, provide the power to make your custom AI applications a reality. #machinelearning #ML #artificialintelligence #AI #mlmodels #arm #ethosu55 #cortexm55 #NPU #neural #neuralprocessing Arm DSP Concepts Edge Impulse Grovety Inc. Nota AI Plumerai Sensory, Inc.

Advanced technologies like deep learning, computer vision, and natural language processing are revolutionizing factory automation. Imagine factories as smart, self-adjusting systems, made possible by these advancements combined with super-fast, reliable connections. However, implementing AI in factories goes beyond just algorithms and connectivity. It requires powerful, energy-saving hardware that can handle massive amounts of data instantly, often in tough working conditions. In this case study, we'll explore how Alif Semiconductor microcontrollers and fusion processors, equipped with specialized AI hardware, empower industries to leverage the power of AI without sacrificing performance, reliability, or energy efficiency. #factoryautomation #AIfactory #intelligentfactory #NPU #neuralprocessing #artificialintelligence #machinelearning #ML

We’re at another great event with EE Times | Electronic Engineering Times for their Embedded IoT Exhibition and the crowd here is buzzing! Alif’s Sampan Chen spoke to the audience about the gap between tinyML and purpose built CPU/GPU processors - a gap that Alif is intent on filling by bringing 32-bit microcontrollers with dedicated NPUs (neural processing units) for on-chip machine learning to the market. #machinelearning #ML #AI #artificialintelligence

The ever-growing number of endpoint devices creates a vast attack surface for cybercriminals. Traditional security measures are no longer enough to defend against increasingly sophisticated network breaches, cloning, and data theft. At Alif Semiconductor, we're revolutionizing endpoint security with a new set of standards for truly secure microcontrollers (MCUs). ️ Our innovative features include: 🔒 Secure Enclave 🔒 Secure Lifecycle States 🔒 TrustZone+ 🔒 Secure Boot Process These features empower you to build secure and trustworthy endpoint devices. Want to dive into these specs further? Download our new whitepaper to learn more about how Alif is securing the future of MCU security. #endpointsecurity #iotsecurity #alifsemiconductor #MCUsecurity

It was a great time at the tinyML Foundation's Innovation Forum in Milan. Alif Semiconductor's Jerome Schang had the exciting opportunity to join a panel and delve into the challenges designers are facing in the world of embedded AI. A major hurdle for designers is being locked into proprietary tool ecosystems, making it hard to switch between different microcontroller families (MCUs) without a complete redesign. Alif tackles this by offering a network of tools built on open-source options that are compatible across any of our MCU families. This allows designers to seamlessly move between devices without starting from scratch, removing a critical barrier of entry to Artificial Intelligence. #tinyml #ml #machinelearning #artificalintelligence

Check out this license plate reader that is running two separate pre-trained machine learning models. The first model identifies if there are license plates in frame, and if so, where they are located. Meanwhile, the second model identifies all symbols in the license place and converts them to text. Multiple plates can be detected and decoded at the same time, with a total inference time, including pre and post processing of image data, of only 200 ms! 🔷 Both models are executed in place from external OSPI connected NVM 🔷 Tensor arena RAM use is approximately 2MB 🔷 Quantized model sizes are 5.2MB for Detection, and 11MB for Decoding 🔷 Helium accelerated ISP pipeline executing on Arm Cortex-M55 core ipXchange Jerome Schang #cortexm55 #cortex #arm Arm #machinelearning #artificalintelligence