--We train and deploy a quantized 1D convolutional neural network model to conduct speech recognition on a highly resource-constrained IoT edge device. This can be useful in various Internet of Things (IoT) applications, such as smart homes and ambient assisted living for the elderly and people with disabilities, just to name a few examples. In this paper, we first create a new dataset with over one hour of audio data that enables our research and will be useful to future studies in this field. Second, we utilize the technologies provided by Edge Impulse to enhance our model's performance and achieve a high Accuracy of up to 97% on our dataset. For the validation, we implement our prototype using the Arduino Nano 33 BLE Sense microcontroller board. This microcontroller board is specifically designed for IoT and AI applications, making it an ideal choice for our target use case scenarios. While most existing research focuses on a limited set of keywords, our model can process 23 different keywords, enabling complex commands. Natural Language Processing (NLP) and Speech Recognition are crucial domains in Artificial Intelligence (AI). While NLP deals with enabling computers to analyze, understand, reason on, and generate human language in textual form, speech recognition is concerned with that in spoken form.

Hornbills, an iconic species of Malaysia's biodiversity, face threats from habitat loss, poaching, and environmental changes, necessitating accurate and real - time population monitoring that is traditionally challenging and resource intensive. The emergence of Tiny Machine Learning (TinyML) offers a chance to transform wildlife monitoring by enabling efficient, real - time data analysis directly on edge devices. Addressing the challenge of wildlife conservation, this research paper explores the pivotal role of machine learning, specifically TinyML, in the classification and monitoring of hornbill calls in Malaysia. Leveraging audio data from the Xeno - canto database, the study aims to develop a speech recognition system capable of identifying and classifying hornbill vocalizations. The proposed methodology involves preprocessing the audio data, extracting features using Mel - Frequency Energy (MFE), and deploying the model on an Arduino Nano 33 BLE, which is adept at edge computing. The research encompasses foundational work, including a comprehensive introduction, literature review, and methodology. The model is trained using Edge Impulse and validated through real - world tests, achieving high accuracy in hornbill species identification. The project underscores the potential of TinyML for environmental monitoring and its broader application in ecological conservation efforts, contributing to both the field of TinyML and wildlife conservation.

The integration of artificial intelligence (AI) into embedded devices, a paradigm known as embedded artificial intelligence (eAI) or tiny machine learning (TinyML), is transforming industries by enabling intelligent data processing at the edge. However, the many tools available in this domain leave researchers and developers wondering which one is best suited to their needs. This paper provides a review of existing eAI tools, highlighting their features, trade-offs, and limitations. Additionally, we introduce EdgeMark, an open-source automation system designed to streamline the workflow for deploying and benchmarking machine learning (ML) models on embedded platforms. EdgeMark simplifies model generation, optimization, conversion, and deployment while promoting modularity, reproducibility, and scalability. Experimental benchmarking results showcase the performance of widely used eAI tools, including TensorFlow Lite Micro (TFLM), Edge Impulse, Ekkono, and Renesas eAI Translator, across a wide range of models, revealing insights into their relative strengths and weaknesses. The findings provide guidance for researchers and developers in selecting the most suitable tools for specific application requirements, while EdgeMark lowers the barriers to adoption of eAI technologies.

Millions of people around the world are infected with mosquito-borne diseases each year. One of the most dangerous species is Aedes aegypti, the main vector of viruses such as dengue, yellow fever, chikungunya, and Zika, among others. Mosquito prevention and eradication campaigns are essential to avoid major public health consequences. In this respect, entomological surveillance is an important tool. At present, this traditional monitoring tool is executed manually and requires digital transformation to help authorities make better decisions, improve their planning efforts, speed up execution, and better manage available resources. Therefore, new technological tools based on proven techniques need to be designed and developed. However, such tools should also be cost-effective, autonomous, reliable, and easy to implement, and should be enabled by connectivity and multi-platform software applications. This paper presents the design, development, and testing of an innovative system named MosquIoT. It is based on traditional ovitraps with embedded Internet of Things (IoT) and Tiny Machine Learning (TinyML) technologies, which enable the detection and quantification of Ae. aegypti eggs. This innovative and promising solution may help dynamically understand the behavior of Ae. aegypti populations in cities, shifting from the current reactive entomological monitoring model to a proactive and predictive digital one.

Edge Impulse is a cloud-based machine learning operations (MLOps) platform for developing embedded and edge ML (TinyML) systems that can be deployed to a wide range of hardware targets. Current TinyML workflows are plagued by fragmented software stacks and heterogeneous deployment hardware, making ML model optimizations difficult and unportable. We present Edge Impulse, a practical MLOps platform for developing TinyML systems at scale. Edge Impulse addresses these challenges and streamlines the TinyML design cycle by supporting various software and hardware optimizations to create an extensible and portable software stack for a multitude of embedded systems. As of Oct. 2022, Edge Impulse hosts 118,185 projects from 50,953 developers.

The past few years have seen an explosion in the use of artificial intelligence on embedded and edge devices. Starting with the keyword spotting models that wake up the digital assistants built into every modern cellphone, "edge AI" products have made major inroads into our homes, wearable devices, and industrial settings. They represent the application of machine learning to a new computational context. ML practitioners are the champions at building datasets, experimenting with different model architectures, and building best-in-class models. ML experts also understand the potential of machine learning to transform the way that humans and technology work together.

Laguna Hills, Calif. – March 6, 2023 – BrainChip Holdings Ltd (ASX: BRN, OTCQX: BRCHF, ADR: BCHPY), the world's first commercial producer of ultra-low power, fully digital, neuromorphic AI IP, today announced the second generation of its Akida platform that drives extremely efficient and intelligent edge devices for the Artificial Intelligence of Things (AIoT) solutions and services market that is expected to be $1T by 2030. This hyper-efficient yet powerful neural processing system, architected for embedded Edge AI applications, now adds efficient 8-bit processing to go with advanced capabilities such as time domain convolutions and vision transformer acceleration, for an unprecedented level of performance in sub-watt devices, taking them from perception towards cognition. The second-generation of Akida now includes Temporal Event Based Neural Nets (TENN) spatial-temporal convolutions that supercharge the processing of raw time-continuous streaming data, such as video analytics, target tracking, audio classification, analysis of MRI and CT scans for vital signs prediction, and time series analytics used in forecasting, and predictive maintenance. These capabilities are critically needed in industrial, automotive, digital health, smart home and smart city applications. The TENNs allow for radically simpler implementations by consuming raw data directly from sensors – drastically reduces model size and operations performed, while maintaining very high accuracy.

Israeli fabless semiconductor company Polyn has announced the availability of neuromorphic analog signal processing (NASP) models for Edge Impulse, a development platform for machine learning on edge devices. Edge impulse provides a way for developers to compare models and their performance, and Polyn is making its models available on the platform to enable such evaluations, CEO and founder Aleksandr Timofeev said in an interview with EE Times Europe. "Polyn is comfortable with this comparison, as it is confident in its promise of offering chips that consume 100 microwatts of power, and no other competitor offers the same," said Timofeev, adding that the company pays a licensing fee to make models available on Edge Impulse. Current ML implementation methods rely on digitizing the generated data and then running them through digital ML frameworks, a process that involves considerable computational power. Processing raw sensor data in analog form can lead to decreased power consumption and increased accuracy for all applications compared with traditional, digital algorithm-based computing, Timofeev said.

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.

Edge Impulse, the leading platform for enabling ML at the edge, and BrainChip, the leading provider of neuromorphic AI IP technology, announced support for deploying Edge Impulse projects on the BrainChip MetaTF platform. Edge Impulse enables developers to rapidly build enterprise-grade ML algorithms, trained on real sensor data, in a low to no code environment. These trained algorithms can now be quantized, optimized and converted to Spiking Neural Networks (SNN), which are compatible and can be deployed with BrainChip Akida devices. This capability is available for new and existing Edge Impulse projects by using the BrainChip MetaTF model deployment block integrated on the platform. This deployment block enables free-tier developers and enterprise developer users to create and validate neuromorphic models for real-world use-cases and deploy on BrainChip Akida development kits.