tinyml

Posted on by Jean-Luc Aufranc (CNXSoft) - 5 Comments on Seeed XIAO BLE – A tiny nRF52840 Bluetooth 5.0 board with (optional) IMU sensor and microphone

Seeed XIAO BLE – A tiny nRF52840 Bluetooth 5.0 board with (optional) IMU sensor and microphone

Seeed XIAO BLE Sense

Seeed Studio has just introduced two new members to their XIAO board family with the Seeed XIAO BLE and XIAO BLE Sense boards equipped with Nordic Semi nRF52840 Bluetooth 5.0 microcontroller, as well as an IMU sensor and microphone on the “Sense” model. Just like the earlier XIAO RP2040 board, the tiny Seed XIAO BLE board can be programmed with Arduino, MicroPython, and CircuityPython, and offers two headers with 7-pin each for GPIOs. What’s really new is the wireless connectivity, the sensors, and a battery charging circuitry. Seeed XIAO BLE specifications: Wireless MCU –  Nordic nRF52840 Arm Cortex-M4F microcontroller @ up to 64 MHz with  1 MB flash, 256 KB SRAM, Bluetooth 5.0, NFC, Zigbee connectivity Storage – 2 MB QSPI flash Expansion I/Os 2x 7-pin headers with 1x UART, 1x I2C, 1x SPI, 1x NFC, 1x SWD, 11x GPIO (PWM), 6x ADC 3.3V I/O voltage (not 5V tolerant) Sensors […]

Posted on by Jean-Luc Aufranc (CNXSoft) - No Comments on Benchmarking TinyML with MLPerf Tiny Inference Benchmark

Benchmarking TinyML with MLPerf Tiny Inference Benchmark

MLPerf Tiny Inference Benchmark

As machine learning moves to microcontrollers, something referred to as TinyML, new tools are needed to compare different solutions. We’ve previously posted some Tensorflow Lite for Microcontroller benchmarks (for single board computers), but a benchmarking tool specifically designed for AI inference on resources-constrained embedded systems could prove to be useful for consistent results and cover a wider range of use cases. That’s exactly what MLCommons, an open engineering consortium, has done with MLPerf Tiny Inference benchmarks designed to measure how quickly a trained neural network can process new data for tiny, low-power devices, and it also includes an optional power measurement option. MLPerf Tiny v0.5, the first inference benchmark suite designed for embedded systems from the organization, consists of four benchmarks: Keyword Spotting – Small vocabulary keyword spotting using DS-CNN model. Typically used in smart earbuds and virtual assistants. Visual Wake Words – Binary image classification using MobileNet. In-home security […]

Posted on by Jean-Luc Aufranc (CNXSoft) - No Comments on Edge Impulse Enables Machine Learning on Cortex-M Embedded Devices

Edge Impulse Enables Machine Learning on Cortex-M Embedded Devices

Edge Impulse

Artificial intelligence used to happen almost exclusively in the cloud, but this introduces delays (latency) for the users and higher costs for the provider, so it’s now very common to have on-device AI on mobile phones or other systems powered by application processors. But recently there’s been a push to bring machine learning capabilities to even lower-end embedded systems powered by microcontrollers, as we’ve seen with GAP8 RISC-V IoT processor or Arm Cortex-M55 core and the Ethos-U55 micro NPU for Cortex-M microcontrollers, as well as Tensorflow Lite. Edge Impulse is another solution that aims to ease deployment of machine learning applications on Cortex-M embedded devices (aka Embedded ML or TinyML) by collecting real-world sensor data, training ML models on this data in the cloud, and then deploying the model back to the embedded device. The company collaborated with Arduino and announced support for the Arduino Nano 33 BLE Sense and […]

Posted on by Jean-Luc Aufranc (CNXSoft) - 4 Comments on ECM3532 AI Sensor Board Features Cortex-M3 MCU & 16-bit DSP “TENSAI” SoC for TinyML Applications

ECM3532 AI Sensor Board Features Cortex-M3 MCU & 16-bit DSP “TENSAI” SoC for TinyML Applications

ECM3532 AI Sensor Board

Eta Compute ECM3532 is a system-on-chip (SoC) with a Cortex-M3 microcontroller clocked at up to 100 Mhz, and NXP CoolFlux 16-bit DSP designed for machine learning on embedded devices, aka TinyML, and part of the company’s TENSAI platform. The chip is also integrated into the ECM3532 AI sensor board featuring two MEMS microphones, a pressure & temperature sensor, and a 6-axis motion sensor (accel/gyro) all powered by a CR2032 coin-cell battery. ECM3532 AI sensor board specifications: SoC – ECM3532 neural sensor processor with Arm Cortex-M3 core @ up to 100 MHz (< 5μA/MHz run mode) combines with 512KB embedded FLASH, 256KB SRAM, and 8KB BootROM + secure bootloader, and NXP CoolFlux 16-bit DSP @ up to 100 MHz with 32KB program memory, 64KB data memory. See the product brief for details. Storage – 64Mbit serial Flash for datalogging Connectivity – Bluetooth 4.2 LE via ABOV Semiconductor A31R118 and PCB antenna […]

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