machine learning News - Page 11 of 21 - CNX Software

December 20, 2022 by Jean-Luc Aufranc (CNXSoft) - 7 Comments

T-Camera S3 – An ESP32-S3 board with camera, display, PIR motion sensor, and microphone

LilyGO has launched a new ESP32-S3 WiFi & BLE camera board with the T-Camera S3 also featuring a small display, a PIR motion sensor, and a microphone, as well as an optional plastic shell. The T-Camera S3 is an evolution of the TTGO T-Camera ESP32 board introduced in 2019 with many of the same features, but the ESP32 microcontroller has been replaced with an ESP32-S3 microcontroller with vector extensions that makes it suitable for machine learning and computer vision applications. The new board also comes with a larger 16 MB SPI flash, more I/Os, and a few other small changes. T-Camera S3 specifications: ESP32-S3-WROOM-1 wireless module SoC – ESP32-S3FN16R8 dual-core Tensilica LX7 microcontroller @ 240 MHz (Note: this SKU is not listed in the official ESP32-S3 datasheet) with 2.4 GHz 802.11n WiFI 4 and Bluetooth 5.0 LE connectivity Memory – 8MB PSRAM Storage – 16MB SPI flash Camera – 2MP […]

December 11, 2022 by Jean-Luc Aufranc (CNXSoft) - 10 Comments

Alif Ensemble Cortex-A32 & Cortex-M55 chips feature Ethos-U55 AI accelerator

Alif Semiconductor’s Ensemble is a family of processors and microcontrollers based on Arm Cortex-A32 and/or Cortex-M55 cores, one or two Ethos-U55 AI accelerators, and plenty of I/Os and peripherals. Four versions are available as follows: Alif E1 single-core MCU with one Cortex-M55 core @ 160 MHz, one Ethos U55 microNPU with 128 MAC/c Alif E3 dual-core MCU with one Cortex-M55 core @ 400 MHz, one Cortex-M55 core @ 160 MHz, one Ethos U55 with 256 MAC/c, one Ethos U55 with 128MAC/c Alif E5 triple-core fusion processor with one Cortex-A32 cores @ 800 MHz, one Cortex-M55 core @ 400 MHz, one Cortex-M55 core @ 160 MHz, one Ethos U55 with 256 MAC/c, one Ethos U55 with 128MAC/c Alif E7 quad-core fusion processor with two Cortex-A32 cores @ 800 MHz, one Cortex-M55 core @ 400 MHz, one Cortex-M55 core @ 160 MHz, one Ethos U55 with 256 MAC/c, one Ethos U55 with […]

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 […]