Espressif releases Arduino ESP32 Core 3.0.0, but PlatformIO support is in doubt

Arduino IDE ESP32-C6 boards

We previously noted the ESP32 Arduino Core 3.0.0 Alpha release added support for ESP32-C6 and ESP32-H2 among other changes. The good news is that Arduino ESP32 Core 3.0.0 is now considered stable, and was released a few days ago based on the ESP-IDF 5.1.4 framework. Users of the Arduino IDE can use it straight away, but as we’ll discuss in more detail below it’s unclear whether PlatformIO will be (officially) supported. There have been many changes since we wrote about the Alpha2 release in November 2023 with 327 commits from 96 contributors. Some of the most recent changes (compared to RC3) include: Updated ESPDuino with extra options (CPU freq and Partition) Add support for WeAct Studio ESP32C3 Attach ETH events at the correct place Enable the possibility to use SPI ETH with only 4 wires Fix ETH.end() Fix ETH.stop() with IDF SPI Nano ESP32: delete programmer.default entry (on main) due […]

M5Stack CoreS3 SE cost-down ESP32-S3 IoT controller features a 2-inch touch display, a microSD card slot, a speaker, two microphones

M5Stack CoreS3 SE ESP32-S3 IoT controller

The M5Stack CoreS3 SE, also called M5CoreS3 SE, is a cost-down version of the M5Stack CoreS3 IoT controller based on the ESP32-S3 wireless microcontroller with a 2-inch capacitive touch display, a microSD card slot, a USB-C port, a speaker, two microphones, and one Grove connector for expansion. The M5Core S3 SE loses the DIN Base so the associate features are gone and DIN rail mounting is not possible by default anymore. That also means the M5Stack CoreS3 SE controller is about twice as thin, and the color is also different (medium grey vs black grey). Major internal changes include the removal of the camera and the three sensors found in the original model. M5Stack CoreS3 SE specifications with highlights in bold and strikethrough showing differences against the CoreS3 model: Wireless MCU – Espressif Systems ESP32-S3FN16R8 CPU – Dual-core 32-bit Xtensa LX7 microcontroller with AI vector instructions up to 240MHz, RISC-V […]

Radxa Fogwise Airbox edge AI box review – Part 1: Specifications, teardown, and first try

Radxa Fogwise Airbox review

Radxa Fogwise Airbox, also known as Fogwise BM168M, is an edge AI box powered by a SOPHON BM1684X Arm SoC with a built-in 32 TOPS TPU and a VPU capable of handling the decoding of up to 32 HD video streams. The device is equipped with 16GB LPDDR4x RAM and a 64GB eMMC flash and features two gigabit Ethernet RJ45 jacks, a few USB ports, a speaker, and more. Radxa sent us a sample for evaluation. We’ll start the Radxa Fogwise Airbox review by checking out the specifications and the hardware with an unboxing and a teardown, before testing various AI workloads with Tensorflow and/or other frameworks in the second part of the review. Radxa Fogwise Airbox specifications The specifications below come from the product page as of May 30, 2024: SoC – SOPHON SG2300x CPU – Octa-core Arm Cortex-A53 processor up to 2.3 GHz VPU Decoding of up to […]

Leveraging GPT-4o and NVIDIA TAO to train TinyML models for microcontrollers using Edge Impulse

Edge Impulse using NVIDIA TAO and GPT-4o LLM to run model on Arduino Nicla Vision

We previously tested Edge Impulse machine learning platform showing how to train and deploy a model with IMU data from the XIAO BLE sense board relatively easily. Since then the company announced support for NVIDIA TAO toolkit in Edge Impulse, and now they’ve added the latest GPT-4o LLM to the ML platform to help users quickly train TinyML models that can run on boards with microcontrollers. What’s interesting is how AI tools from various companies, namely NVIDIA (TAO toolkit) and OpenAI (GPT-4o LLM), are leveraged in Edge Impulse to quickly create some low-end ML model by simply filming a video. Jan Jongboom, CTO and co-founder at Edge Impulse, demonstrated the solution by shooting a video of his kids’ toys and loading it in Edge Impulse to create an “is there a toy?” model that runs on the Arduino Nicla Vision at about 10 FPS. Another way to look at it […]

Arm unveils Cortex-X925 and Cortex-A725 CPUs, Immortalis-G925 GPU, Kleidi AI software

Arm SoC with Cortex-X925 Cortex-A725 Cortex-A520 CPU with Immortalis-G925 GPU

Arm has just announced new Armv9 CPUs and Immortalis GPUs for mobile SoCs, as well as the Kleidi AI software optimized for Arm CPUs from Armv7 to Armv9 architectures. New Armv9.2 CPU cores include the Cortex-X925 “Blackhawk” core with significant CPU and AI performance improvements, the Cortex-A725 with improved performance efficiency, and a refreshed version of the Cortex-A520 providing 15 percent efficiency improvements. Three new GPUs have also been introduced namely the up-to-14-core Immortalis-G925 flagship GPU which delivers up to 37% 3D graphics performance improvements over last year’s 12-core Immortalis-G720, the Mali-G725 with 6 to 9 cores for premium mobile handsets, and the Mali-G625 GPU with one to five cores for smartwatches and entry-level mobile devices. Arm Cortex-X925 The Arm Cortex-X925 delivers 36 percent single-threaded peak performance improvements in Geekbench 6.2 against a Cortex-X4-based Premium Android smartphone, and about 41 percent better AI performance using the time-to-first token of tiny-LLama […]

Ochin V2 tiny Raspberry Pi CM4 robotics carrier board gets micro HDMI port, Fast Ethernet support

Ochin V2 CM4 robotics carrier board and USB-C board

Ochin V2 is an update to the tiny Ochin Raspberry Pi CM4 carrier board for robotics applications and drones that adds a micro HDMI port, support for Fast Ethernet through pads or a GHS connector (no RJ45 connector), two user LEDs, and a few other changes. The form factor remains the same at just 55 x 40 x 4.7mm, or about the size of a Raspberry Pi Compute Module 4, which in combination with a range of USB, UART, I2C, and SPI interfaces, makes it an ideal candidate for space-constrained applications such as robotics system or UAVs. Ochin V2 specifications (differences against Ochin v1 shown in bold or strikethrough): Supported modules – Raspberry Pi CM4 with Broadcom BCM2711 quad-core Cortex-A72 processor, up to 8GB RAM, up to 32GB eMMC flash (the CM4 Lite is not supported since there’s no microSD card on the board), 4Kp60 H.265 decode, 1080p30 H.264 encode, […]

7-inch ESP32-S3 touchscreen display exposes RS485, CAN Bus, I2C, UART, and analog sensor interfaces

ESP32-S3 7-inch Touch LCD

Waveshare ESP32-S3-Touch-LCD-7 is an ESP32-S3 powered WiFi 4 and Bluetooth 5 LE 7-inch touchscreen display with plenty of expansion interfaces such as RS485, CAN Bus, I2C, UART, and Analog input that can be used to develop various HMI applications We’ve written about many ESP32 boards with displays, but most are small displays under 3-inch, and larger displays are more of a rarity except for ESP32 e-Paper displays such as the Inkplate 10 or LILYGO 7.5-inch e-Paper display. Most are based on ESP32-S3 since it comes with an RGB LCD interface, and the only other 7-inch ESP32-S3 touchscreen display we’ve looked into is the Elecrow 7.0-inch display with specifications similar to the Waveshare ESP32-S3-Touch-LCD-7, but fewer I/O headers. Waveshare ESP32-S3-Touch-LCD-7: Wireless module – ESP32-S3-WROOM-1 MCU – ESP32-S3N8R8 dual-core Tensilica LX7 up to 240 MHz with 512KB SRAM, 8MB PSRAM, 8MB flash Wireless – 2.4 GHz WiFi 4 and Bluetooth LE 5 […]

picoLLM is a cross-platform, on-device LLM inference engine

picoLLM Raspberry Pi 5

Large Language Models (LLMs) can run locally on mini PCs or single board computers like the Raspberry Pi 5 but with limited performance due to high memory usage and bandwidth requirements. That’s why Picovoice has developed the picoLLM Inference Engine cross-platform SDK optimized for running compressed large language models on systems running Linux (x86_64), macOS (arm64, x86_64), and Windows (x86_64), Raspberry Pi OS on Pi 5 and 4, Android and iOS mobile operating systems, as well as web browsers such as Chrome, Safari, Edge, and Firefox. Alireza Kenarsari, Picovoice CEO, told CNX Software that “picoLLM is a joint effort of Picovoice deep learning researchers who developed the X-bit quantization algorithm and engineers who built the cross-platform LLM inference engine to bring any LLM to any device and control back to enterprises”. The company says picoLLM delivers better accuracy than GPTQ when using Llama-3.8B MMLU (Massive Multitask Language Understanding) as a […]

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