machine learning News - Page 13 of 22 - CNX Software

December 9, 2022December 9, 2022 by Jean-Luc Aufranc (CNXSoft) - 6 Comments

Bee Motion S3 – An ESP32-S3 board with a PIR motion sensor (Crowdfunding)

The Bee Motion S3 is an ESP32-S3 WiFi and Bluetooth IoT board with a PIR motion sensor beside the more usual I/Os, Qwiic connector, USB-C port, and LiPo battery support. It is at least the third PIR motion wireless board from Smart Bee Designs, as the company previously introduced the ESP32-S2 powered Bee Motion board and the ultra-small Bee Motion Mini with an ESP32-C3 SoC. The new Bee Motion S3 adds a few more I/Os, a light sensor, and the ESP32-S3’s AI vector extensions could potentially be used for faster and/or lower-power TinyML processing. Bee Motion S3 specifications: Wireless module – Espressif Systems ESP32-S3-MINI-1 module (PDF datasheet) based on ESP32-S3 dual-core Xtensa LX7 microcontroller with 512KB SRAM, 384KB ROM, WiFi 4 and Bluetooth 5.0 connectivity, and equipped with 8MB of QSPI flash and a PCB antenna USB – 1x USB Type-C port for power and programming Sensors PIR sensor S16-L221D […]

December 7, 2022 by Jean-Luc Aufranc (CNXSoft) - 2 Comments

Hand Gesture Recognition on ESP32-S3 with the ESP-DL library

Ali Hassan Shah has deployed a deep learning model for hand gesture recognition on the ESP32-S3-EYE board using the ESP-DL library and achieved AI-powered hand recognition with a 0.7-second latency on the ESP32-S3 camera board. Last year, Espressif released the ESP-DL library for the ESP32-S3 microcontroller with AI vector extensions, as well as ESP32 and ESP32-S2, along with a face detection demo that ran much faster on the ESP32-S3. Ali rolled out his own solution for AI gesture recognition and provided a step-by-step tutorial along the way. The main steps to deploying a custom model with the ESP-DL library include: Model Development that involves Getting or creating datasets. In this case, downloaded from Kaggle with 6 gestures namely Palm, I, Thumb, Index, Ok, and C. Testing, training, and calibrating the datasets Building a (CNN) Model Training a Model Saving a Model to the Hierarchical Data format (.h5) Converting the H.5 […]

November 14, 2022November 18, 2022 by Jean-Luc Aufranc (CNXSoft) - 20 Comments

Sipeed M1s & M0sense – Low-cost BL808 & BL702 based AI modules (Crowdfunding)

Sipeed has launched the M1s and M0Sense AI modules. Designed for AIoT application, the Sipeed M1s is based on the Bouffalo Lab BL808 32-bit/64-bit RISC-V wireless SoC with WiFi, Bluetooth, and an 802.15.4 radio for Zigbee support, as well as the BLAI-100 (Bouffalo Lab AI engine) NPU for video/audio detection and/or recognition. The Sipeed M0Sense targets TinyML applications with the Bouffa Lab BL702 32-bit microcontroller also offering WiFi, BLE, and Zigbee connectivity. Sipeed M1s AIoT module The Sipeed M1S is an update to the Kendryte K210-powered Sipeed M1 introduced several years ago. Sipeed M1s module specifications: SoC – Bouffalo Lab BL808 with CPU Alibaba T-head C906 64-bit RISC-V (RV64GCV+) core @ 480MHz Alibaba T-head E907 32-bit RISC-V (RV32GCP+) core @ 320MHz 32-bit RISC-V (RV32EMC) core @ 160 MHz Memory – 768KB SRAM and 64MB embedded PSRAM AI accelerator – NPU BLAI-100 (Bouffalo Lab AI engine) for video/audio detection/recognition delivering up […]

November 2, 2022November 2, 2022 by Jean-Luc Aufranc (CNXSoft) - 10 Comments

Quadric Chimera GPNPU IP combines NPU, DSP, and real-time CPU into one single programmable core

A typical chip for AI or ML inference would include an NPU, a DSP, a real-time CPU, plus some memory, an application processor, an ISP, and a few more IP blocks. Quadric Chimera GPNPU (general purpose neural processor unit) IP combines the NPU, DSP, and real-time CPU into one single programmable core. According to Quadric, the main benefit of such design is simplifying system-on-chip (SoC) hardware design and subsequent software programming once the chip is available thanks to a unified architecture for machine learning inference as well as pre-and-post processing. Since the core is programmable it should also be future-proof. Three “QB series” Chimera GPNPU cores are available: Chimera QB1 – 1 TOPS machine learning, 64 GOPS DSP capability Chimera QB4 – 4 TOPS ML, 256 GOPS DSP Chimera QB16 – 16 TOPS ML, 1 TOPS DSP Quadric says the Chimera cores can be used with any (modern) manufacturing process […]

October 31, 2022 by Jean-Luc Aufranc (CNXSoft) - 3 Comments

TinyML-CAM pipeline enables 80 FPS image recognition on ESP32 using just 1 KB RAM

TinyML-CAM image recognition microcontroller boards

The challenge with TinyML is to extract the maximum performance/efficiency at the lowest footprint for AI workloads on microcontroller-class hardware. The TinyML-CAM pipeline, developed by a team of machine learning researchers in Europe, demonstrates what’s possible to achieve on relatively low-end hardware with a camera. Most specifically, they managed to reach over 80 FPS image recognition on the sub-$10 ESP32-CAM board with the open-source TinyML-CAM pipeline taking just about 1KB of RAM. It should work on other MCU boards with a camera, and training does not seem complex since we are told it takes around 30 minutes to implement a customized task. The researchers note that solutions like TensorFlow Lite for Microcontrollers and Edge Impulse already enable the execution of ML workloads, onMCU boards, using Neural Networks (NNs). However, those usually take quite a lot of memory, between 50 and 500 kB of RAM, and take 100 to 600 ms […]

October 27, 2022October 27, 2022 by Jean-Luc Aufranc (CNXSoft) - 5 Comments

Easily add face detection to your project with the Person Sensor module

It’s now much easier to AI features to your project thanks to better tools, but as we’ve experienced when trying out Edge Impulse machine learning platform on the XIAO BLE Sense board, it still requires some effort and the learning curve may be higher than some expect. But for common tasks like face detection, there’s no reason for the solution to be hard-to-use or expensive, and Pete Warden (Useful Sensors) has designed the $10 Person Sensor fitted with a camera module pre-programmed with algorithms that detect nearby faces and reports the results over an I2C interface. Person Sensor specifications: ASIC – Himax HX6537-A ultra-low-power AI accelerator @ 400 MHz with 2MB SRAM, 2MB flash Camera Image Sensor – 110 degrees FOV Image scan rate – 7Hz with no facial recognition Image scan rate – 5Hz with facial recognition active Host interface Qwiic connector for the I2C interface up to […]

October 19, 2022October 19, 2022 by Jean-Luc Aufranc (CNXSoft) - 1 Comment

Google KataOS – A secure OS for embedded systems written in Rust (mostly)

Google Research has been working on its own Rust-based operating system called KataOS and designed to secure embedded systems that run Machine Learning (ML) applications. There has been a lot of talk about the Rust programming language in recent times, since it offers about the same level of performance as C programming but helps programmers write more secure code with built-in prevention against buffer overflows for instance. It has gained a lot of traction over the years, and Linux 6.1 will be the first kernel release to include Rust code. Google Research noticed that system security is often treated as a feature that can be added to existing systems either by software or an extra security chip. But in a world, where more and more of our private data is exposed to the world through the Internet, it is not good enough, so the company developed KataOS open-source, secure operating […]

September 30, 2022 by Jean-Luc Aufranc (CNXSoft) - 5 Comments

Nuvoton NuMicro MA35D1 Arm Cortex-A35/M4 microprocessor to power Linux edge IIoT gateways

Novoton NuMicro MA35D1 microprocessor features two Arm Cortex-A35 cores, one Arm Cortex-M4 real-time core, and two Ethernet interfaces for Linux-based edge IIoT gateway. The SoC also is offered in variants supporting external DDR memory or integrated up to 512MB RAM, 154 or 208 GPIOs, and an optional “Enhanced ADC”. The MA35D1 also comes with a TFT interface for up to 1920×1080 displays, several hardware security features, and the company says the microprocessor facilitates Tiny AI/ML for edge computing despite not integrating an AI accelerator. Nuvoton NuMicro MA35D1 key features and specifications: CPU sub-system 2x Cortex-A35 cores running at up to 800 MHz Cortex-M4 real-time core at up to 180 MHz Memory sub-system On-chip 384 KB SRAM (Cortex-A35 256 KB + Cortex-M4 128 KB) Multi-Chip Package (MCP) DDR up to 512MB External DDR interface for MA35D16A087C SKU Storage Quad SPI NAND Flash Controller Secure Digital Host Controller (SDHC) Display and Video […]