machine learning News - Page 12 of 18 - CNX Software

June 17, 2021 by Jean-Luc Aufranc (CNXSoft) - 1 Comment

Picovoice offline Voice AI engine now works on Arduino

Last year, I wrote about Picovoice support for Raspberry Pi enabling custom wake-word and offline voice recognition to control the board with voice commands without relying on the cloud. They used ReSpeaker 4-mic array HAT to add four “ears” to the Raspberry Pi SBC. I also tried to generate a custom wake-word using the “Picovoice Console” web interface, and I was able to use “Dear Master” within a few minutes on my computer. No need to provide thousands of samples, or wait weeks before getting a custom wake-word. It’s free for personal projects. But the company has now added Picovoice to Arduino, or more exactly Arduino Nano 33 BLE Sense powered by a Nordic Semi nRF52480 Arm Cortex-M4F microcontroller, and already equipped with a digital microphone, so no additional hardware is required for audio capture. To get started, you’d just need to install the Picovoice Arduino library, load the sample […]

April 13, 2021April 13, 2021 by Jean-Luc Aufranc (CNXSoft) - 2 Comments

Arducam Pico4ML Board – TinyML on Raspberry Pi RP2040 with QVGA Camera & Display

A few months ago, ArduCAM demonstrated person detection on Raspberry Pi Pico with Arducam camera using TensorFlow Lite, and later we noted more work was being performed to bring machine learning to RP2040 platforms, notably with the development of Arducam Pico4ML board with a built-in camera and display. At the time, i..e last month, all we had were some renders of the board, but now Arducam Pico4ML pre-orders have launched for $49.99 on UCTRONICS and Tindie stores. Shipping is scheduled to start at the end of the month, so let’s have a closer look. Arducam Pico4ML TinyML devkit specifications: Microcontroller – Raspberry Pi RP2040 dual-core Cortex-M0+ MCU with 264 KB of embedded SRAM Storage – 2MB SPI flash Display – 0.96-inch LCD SPI Display (ST7735) with 160 x 80 resolution Camera – HiMax HM01B0 QVGA camera (320×240 @ 60fps) Audio – Built-in microphone Sensor – IDK ICM-20948 9-axis IMU (gyroscope, […]

April 4, 2021October 27, 2021 by Jean-Luc Aufranc (CNXSoft) - No Comments

Raspberry Pi CM4 based predictive maintenance gateway features Google Coral Edge AI accelerator

Techbase IModGate-AI predictive maintenance gateway

Techbase had already integrated the Raspberry Pi CM4 module into several industrial products including Modberry 500 CM4 DIN Rail industrial computer, ModBerry AI GATEWAY 9500-CM4 with a Google Edge TPU, and ClusBerry 9500-CM4 that combines several Raspberry Pi CM4 modules into a DIN Rail mountable system. The company has now announced another Raspberry Pi Compute Module 4 gateway – iModGATE-AI – specially designed for failure monitoring and predictive maintenance of IoT installations, which also embeds a Google Coral Edge TPU AI module to accelerate computer vision. iModGATE-AI gateway specifications: SoM – Raspberry Pi Compute Module 4 with up to 32GB eMMC AI accelerator – Google Coral Edge TPU AI module Video Output – HDMI port Connectivity – Gigabit Ethernet USB – USB 2.0 port Sensors – 9-axis motion tracking module with 3-axis gyroscope with Programmable FSR, 3-axis accelerometer with Programmable FSR, 3-axis compass (magnetometer) Expansion 2x 16-pin block terminal Advanced […]

March 23, 2021March 23, 2021 by Jean-Luc Aufranc (CNXSoft) - 1 Comment

Made in Thailand CorgiDude RISC-V AI board aims to teach machine learning

There’s a relatively small but active maker community in Thailand, and we’ve covered or even reviewed some made in Thailand boards including ESP8266 and ESP32 boards, a 3G Raspberry Pi HAT, and KidBright education platform among others. MakerAsia has developed CorgiDude, a board based on the version of Sipeed M1 RISC-V AI module with built-in WiFi, and part as a kit with a camera and a display used to teach machine learning and artificial intelligence with MicroPython or C/C++ programming. CorgiDude board specifications: AI Wireless Module – Sipeed M1W Module with Kendryte K210 dual-core 64-bit RISC-V RV64IMAFDC CPU @ 400Mhz with FPU, various AI accelerators (KPU, FFT accelerator…), 8MiB on-chip SRAM Espressif ESP8285 single-core 2.4 GHz WiFi 4 SoC plus IPEX antenna connector Storage – MicroSD card slot Camera I/F for 2MP OV2640 sensor up to 1280 × 1024 (SXGA) @ 30 fosm SVGA @ 30 fps, or CIF @ […]

March 16, 2021 by Jean-Luc Aufranc (CNXSoft) - No Comments

Sparkfun Thing Plus – Quicklogic EOS S3 Arm eFPGA board launched in Crowd Supply

SparkFun Electronics is a well-known electronics retailer that usually sells its in-house developed or third-party boards through its own online store. But this time around, the company decided to launch “Sparkfun Thing Plus – Quicklogic EOS S3” through Crowd Supply crowdfunding platform. The board is based on QuickFeather board designed with the same Quicklogic EOS S3 Arm Cortex-M4 plus embedded FPGA SoC, but follows Sparkfun’s Thing Plus form factor with a Qwiic connector and a different mix of sensors. Sparkfun Thing Plus – Quicklogic EOS S3 (QTPLUS-1.0) board specifications: SoC – QuickLogic EOS S3 MCU + eFPGA SoC with Arm Cortex-M4F Microcontroller up to 80 MHz, up to 512 Kb SRAM, and an embedded FPGA (eFPGA) with 2400 effective logic cells, 64 Kb RAM Storage – 16 Mbit SPI NOR flash (GigaDevice GD25Q16CEIGR) Sensors STMicro LIS2DH12TR accelerometer Digital pulse density modulation (PDM) microphone with Wake-on-Sound (WoS) feature: Vesper VM3011-U1 Expansion […]

March 12, 2021 by Saumitra Jagdale - 27 Comments

Machine Learning on Raspberry Pi Pico, RP2040, and future RPi MCUs

Although the Raspberry Pi Pico comes with the RP2040 chip that lacks the performance to implement machine learning inference for its applications. However, we saw a person detection use case through ArduCAM and TensorFlow lite interface. But, the processing performance of the use case was on the slower side. Additionally, a recent Eben Upton presentation also unveiled that due to low power requirements the board compensates the processing efficiency. Hence, it offers low-performance for edge inference and machine learning use cases. Eben Upton’s teaser on improvement in machine learning and the future scope of “Pi Silicon” revealed potential growth and development in edge inference applications. The demand for RP2040 boards has given rise to the market necessity for more boards. This demand can only be fulfilled if more boards with RP2040 chip are available in the market and company “partners such as Adafruit, Pimoroni, Adafruit and Sparkfun are start releasing […]

January 28, 2021May 13, 2021 by Jean-Luc Aufranc (CNXSoft) - 4 Comments

Arduino Portenta H7 Gets Embedded Vision Shield with Ethernet or LoRa Connectivity

[Update January 28, 2021: The LoRa version of Portenta Vision Shield is now available] Announced last January at CES 2020, Arduino Portenta H7 is the first board part industrial-grade “Arduino Pro” Portenta family. The Arduino MKR-sized MCU board has plenty of processing power thanks to STMicro STM32H7 dual-core Arm Cortex-M7/M4 microcontroller. It was launched with a baseboard providing access to all I/Os and ports like Ethernet, USB, CAN bus, mPCIe socket (USB), etc… But as AI moves to the very edge, it makes perfect sense for Arduino to launch Portenta Vision Shield with a low-power camera, two microphones, and a choice of wired (Ethernet) or wireless (LoRA) connectivity for machine learning applications. Portenta Vision Shield key features and specifications: Storage – MicroSD card socket Camera – Himax HM-01B0 camera module with 324 x 324 active pixel resolution with support for QVGA Image sensor – High sensitivity 3.6μ BrightSense pixel technology […]

January 11, 2021January 10, 2021 by Saumitra Jagdale - No Comments

DBM10 AI SoC is optimized for battery-powered voice and sensor processing

DSP Group announced DBM10 a low-power AI/ML-enabled dual-core SoC. The SoC is equipped with a DSP (Digital Signal Processor) and a dedicated nNetLite NN (Neural Network) processor that improves voice and sensor processing and ensures low-power consumption when working with sufficient-sized neural networks. Key Specifications of NN Processor Form factor: ~4 mm2 Ultra-low-power inference consumption – ~500 µW (typical) for voice NN algorithms Runs Hello Edge 30-word detection model @ 1 MHz (125 MHz available) Allows porting of large models (10s of megabytes) without significant accuracy loss using model optimization and compression. DBM10 AI SoC uses the combined functioning of machine learning, voice, and sensor parameters. This includes voice trigger (VT), voice authentication (VA), voice command (VC), noise reduction (NR), acoustic echo cancellation (AEC), sound event detection (SED), proximity and gesture detection, sensor data processing, and equalization. The DBM10 is suitable for battery-operated devices like smartphones, tablets, and wearables. It […]