Aspinity AB2 AML100 Arduino Shield supports ultra-low-power analog machine learning

Aspinity AB2 AML100 analog machine learning Arduino Shield

Aspinity AB2 AML100 is an Arduino Shield based on the company’s AML100 analog machine learning processor that reduces power consumption by 95 percent compared to equivalent digital ML processors, and the shield works with Renesas Quick-Connect IoT platform or other development platforms with Arduino Uno Rev3 headers. The AML100 analog machine learning processor is said to consume just 15µA for sensor interfacing, signal processing, and decision-making and operates completely within the analog domain offloading most of the work from the microcontroller side that can stay its lowest power state until an event/anomaly is detected. Aspinity AB2 AML100 Arduino Shield specifications: ML chip – Aspinity AML100 analog machine learning chip Software programmable analogML core with an array of configurable analog blocks (CABs) with non-volatile memory and analog signal processing Processes natively analog data Near-zero power for inference and events detection Consumes <20µA when always-sensing Reduces analog data by 100x Supports up […]

Arduino GIGA R1 WiFi board launches with STM32H7 MCU, up to 76 I/O pins

Arduino GIGA R1 WiFi board brings the STM32H7 dual-core Cortex-M7/M4 microcontroller found in the Portenta H7 boards to the larger Arduino Mega/Due form factor with up to 76 GPIO pins. As its name implies, the board also comes with a WiFi 4 (and Bluetooth 5.1) module, as well as an audio jack, a USB Type-C port for programming, a USB 2.0 Type-A host port, and extra connectors for a display and a camera. Arduino GIGA R1 WiFi board specifications: Microcontroller – STMicro STM32H747XI Cortex-M7 @ 480 MHz + M4 @ 200 MHz MCU  with 2MB dual-bank Flash memory, 1 MB RAM, Chrom-ART graphical hardware accelerator System Memory – 8MB SDRAM Storage – 16MB QSPI NOR flash Connectivity – 2.4GHz WiFi 802.11b/g/n up to 65 Mbps and Bluetooth 5.1 BR/EDR/LE via Murata 1DX module Display – 20-pin header (J5) Camera – 20-pin Arducam camera header (J6) USB 1x USB Type-C port […]

Amlogic S928X specifications – A penta-core Arm Cortex-A76/A55 SoC with Mali-G57 GPU, 3.2 TOPS NPU

Amlogic S928X Cortex-A76/A55 will soon power some 8K TV boxes running Android TV from SDMC and others, and we now have detailed specifications as a “Quick Reference Manual” dropped in my inbox last night. While like most Amlogic processors, the S928X is designed for TV boxes, the penta-core SoC may end up in affordable yet powerful single board computers with features such as HDMI 2.1a, Gigabit Ethernet, PCIe 2.0 or USB 3.0. Amlogic S928X specifications: CPU sub-system 1x Arm Cortex-A76 core and 4x Arm Cortex-A55 cores in big.LITTLE configuration NEON and Crypto extensions Private L2 cache and unified system L3 cache Build-in RISC-V core for system control processing GPUs Arm Mali-G57 MC2 3D GPU with support for OpenGL ES 3.2, Vulkan 1.2, and OpenCL 2.0 2.5D graphics processor for scaling, alpha, rotation, and color space conversion operations VPU Amlogic Video Engine (AVE) with dedicated hardware decoders and encoders Decoding AV1 […]

Arduino Nano 33 BLE Sense Rev2 switches to BMI270 & BMM150 IMUs, HS3003 temperature & humidity sensor

Arduino Nano 33 BLE Sense Rev2 is a new revision of the Nano 33 BLE Sense machine learning board with basically the same functionality but some sensors have changed along with some other modifications “to improve the experience of the users”. The main changes are that STMicro LSM9DS1 9-axis IMU has been replaced by two IMUs from Bosch SensorTech, namely the BMI270 6-axis accelerometer and gyroscope, and the BMM150 3-axis magnetometer, a Renesas HS3003 temperature & humidity sensor has taken the place of an STMicro HTS221, and the microphone is now an MP34DT06JTR from STMicro instead of an MP34DT05. All of the replaced parts are from STMicro, so it’s quite possible the second revision of the board was mostly to address supply issues. Arduino Nano 33 BLE Sense Rev2 (ABX00069) specifications: Wireless Module – U-blox NINA-B306 module powered by a Nordic Semi nRF52840 Arm Cortex-M4F microcontroller @ 64MHz with 1MB […]

M5Stamp S3 WiFi and BLE IoT module offers up to 23 GPIOs through 2.54mm and 1.27mm pitch holes

M5Stamp S3, aka M5Stamp ESP32S2 or Stamp S3, is a tiny ESP32-S3 WiFi & Bluetooth LE (BLE) IoT module with a USB Type-C port, over 20 I/Os available through 2.54mm and 1.27mm pitch headers and castellated holes, and a heat-resistant cover. Many of the “new” ESP32-S3 hardware platforms launches these days are often updates from an ESP32 design, and the M5Stack’s M5Stamp S3 is no exception building on the original M5Stamp Pico, and its ESP32-C3 variants, namely M5Stamp C3 and C3U. M5Stamp S3 specifications: WiSoC – Espressif Systems ESP32-S3FN8 dual-core 32-bit Xtensa LX7 microcontroller with AI vector instructions up to 240MHz, RISC-V ULP co-processor, 512KB SRAM, 2.4GHz WiFi 4 (802.11b/g/n), Bluetooth 5.0 BLE + Mesh, 8MB flash as found in the M5Stack ATOMS3 (Lite). Connectivity 2.4 GHz WiFi 4, 20 MHz and 40 MHz bandwidth, IEEE 802.11 b/g/n protocol, up to 150 Mbps Bluetooth 5, Bluetooth Mesh, with supports for […]

Eoxys Xeno+ Nano ML board combines NuMicro M2354 or STM32L4 MCU with Talaria TWO ultra low power WiFi & BLE 5.0 module

Eoxys Xeno+ Nano ML is a wireless machine learning (ML) board with either Nuvoton NuMicro M2354 or STMicro STM32L4 microcontroller, InnoPhase IoT’s Talaria TWO ultra-low power Wi-Fi and BLE 5.0 module, and the Syntiant Core 2 NDP120 neural decision processor we first noticed in the Arduino Nicla Voice module a few weeks ago. The boards/modules are designed for intelligent and secure IoT devices for smart home, industrial, and medical automation applications, and the company claims it can be used in Wi-Fi IoT sensors with up to 10+ years thanks to the low-power chips and circuitry used in the design. Eoxys Xeno+ Nano ML specifications: General purpose MCU (one or the other) STMicro STM32L4 Arm Cortex-M4 microcontroller at 80MHz with 1MB flash, 128KB/352KB SRAM Nuvoton NuMicro M2354 Arm Cortex-M23 microcontroller at 96MHz with 1MB flash, 128KB SRAM. Wireless module Innophase Talaria TWO ultra-low-power 2.4GHz 802.11b/n/g WiFi 4 and Bluetooth LE 5.0 […]

M5Stack ATOMS3 Lite is a tiny ESP32-S3 IoT controller with WiFi, Bluetooth, and an infrared transmitter

ATOMS3 Lite is the latest ESP32-S3 IoT platform from the M5Stack Atom series wireless programmable controllers, doing without the 0.85-inch display and IMU sensor  found in the ATOM S3 development kit simply using an RGB LED instead. M5Stack ATOM S3 Lite features the ESP32-S3FN8 WiFi and Bluetooth SoC with 8MB SPI flash, an infrared transmitter, a USB-C port for power and programming, a few I/O pins, user and reset buttons all in just a 24x24x9.5mm housing. ATOMS3 Lite specifications: Wireless MCU – Espressif Systems ESP32-S3FN8 dual-core 32-bit Xtensa LX7 microcontroller with AI vector instructions up to 240MHz, RISC-V ULP co-processor, 512KB SRAM,  2.4GHz WiFi 4 (802.11b/g/n), Bluetooth 5.0 BLE + Mesh, 8MB flash Antenna – Internal “3D” antenna Expansion 9x pins with G5, G6, G7, G8, G38, and G39 GPIOs, 5V, 3.3V, and GND 4-pin Grove connector Misc IR LED (infrared transmitter/blaster) WS2812B-2020 RGB LED Reset and user buttons M.2 […]

Coral Dev Board Micro combines NXP i.MX RT1176 MCU with Edge TPU in Pi Zero form factor

Coral Dev Board Micro is the latest iteration of Google’s Edge AI devkit with an NXP i.MX RT1176 Cortex-M7/M4 crossover processor/microcontroller coupled with the company’s 4 TOPS Edge TPU, a camera, and a microphone in a board that’s about the size of a Raspberry Pi Zero SBC. The new board follows the original NXP i.MX 8M-based Coral Dev board that was introduced in 2019, and Coral Dev Board mini based on MediaTek MT8167S processor launched in 2020, and keeps with the trend of providing more compact solutions with lower-end host processors for edge AI. Coral Dev Board Micro specifications: MCU – NXP i.MX RT1176 processor with an Arm Cortex-M7 core @ up to 1 GHz, Cortex-M4 core up to 400 MHz, 2MB internal SRAM, 2D graphics accelerators; System Memory – 512 Mbit (64 MB) RAM Storage – 1 Gbit (128 MB) flash memory ML accelerator – Coral Edge TPU coprocessor […]

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