Uncategorized

• Uncategorized

  • OpenMV - A camera that runs with MicroPython on ARM Cortex M6/M7 and great support for computer vision algorithms. Now with [support for Tensorflow Lite too](https://openmv.io/blogs/news/tensorflow-lite-and-person-detection).
  • JeVois - A TensorFlow-enabled camera module.
  • UP AI Edge - Line of products based on Intel Movidius VPUs (including Myriad 2 and Myriad X) and Intel Cyclone FPGAs.
  • DepthAI - An embedded platform for combining Depth and AI, built around Myriad X
  • NVIDIA Jetson - High-performance embedded system-on-module to unlock deep learning, computer vision, GPU computing, and graphics in network-constrained environments.
  • Artificial Intelligence Radio - Transceiver (AIR-T) - High-performance SDR seamlessly integrated with state-of-the-art deep learning hardware.
  • Kendryte K510 - Tri-core RISC-V processor clocked with AI accelerators.
  • Sipeed M1 - Based on the Kendryte K210, the module adds WiFi connectivity and an external flash memory.
  • UNIT-V - AI Camera powered by Kendryte K210 (lower-end M5StickV)
  • GreenWaves GAP8 - RISC-V-based chip with hardware acceleration for convolutional operations.
  • Ultra96 - Embedded development platform featuring a Xilinx UltraScale+ MPSoC FPGA.
  • Apollo3 Blue - SparkFun Edge Development Board powered by a Cortex M4 from Ambiq Micro.
  • Google Coral - Platform of hardware components and software tools for local AI products based on Google Edge TPU coprocessor.
  • ARM microNPU - Processors designed to accelerate ML inference (being the first one the Ethos-U55).
  • Espressif ESP32-S3 - SoC similar to the well-known ESP32 with support for AI acceleration (among many other interesting differences).
  • Maxim MAX78000 - SoC based on a Cortex-M4 that includes a CNN accelerator.
  • Beagleboard BeagleV - Open Source RISC-V-based Linux board that includes a Neural Network Engine.
  • Syntiant TinyML - Development kit based on the Syntiant NDP101 Neural Decision Processor and a SAMD21 Cortex-M0+.
  • TensorFlow Lite for Microcontrollers - Port of TF Lite for microcontrollers and other devices with only kilobytes of memory. Born from a [merge with uTensor](https://os.mbed.com/blog/entry/uTensor-and-Tensor-Flow-Announcement/).
  • CMSIS NN - A collection of efficient neural network kernels developed to maximize the performance and minimize the memory footprint of neural networks on Cortex-M processor cores.
  • ST X-CUBE-AI - Toolkit for generating NN optimiezed for STM32 MCUs.
  • ST NanoEdgeAIStudio - Tool that generates a model to be loaded into an STM32 MCU.
  • uTVM - *MicroTVM* is an open source tool to optimize tensor programs.
  • Edge Impulse - Interactive platform to generate models that can run in microcontrollers. They are also quite active on social netwoks talking about recent news on EdgeAI/TinyML.
  • Qeexo AutoML - Interactive platform to generate AI models targetted to microcontrollers.
  • Benchmarking Edge Computing (May 2019)
  • Why Machine Learning on The Edge?
  • Tutorial: Low Power Deep Learning on the OpenMV Cam
  • tinyML Summit - Annual conference and monthly meetup celebrated in California, USA. Talks and slides are usually [available from the website](https://www.tinymlsummit.org/#meetups).
  • Kendryte K510 - Tri-core RISC-V processor clocked with AI accelerators.
  • Gyrfalcon Technology Lighspeeur - Family of chips optimized for edge computing.
  • Why Machine Learning on The Edge?
  • Tutorial: Low Power Deep Learning on the OpenMV Cam
  • Why Machine Learning on The Edge?
  • Tutorial: Low Power Deep Learning on the OpenMV Cam
  • Why Machine Learning on The Edge?
  • Tutorial: Low Power Deep Learning on the OpenMV Cam
  • Why Machine Learning on The Edge?
  • Tutorial: Low Power Deep Learning on the OpenMV Cam
  • Why Machine Learning on The Edge?
  • Tutorial: Low Power Deep Learning on the OpenMV Cam
  • Why Machine Learning on The Edge?
  • Tutorial: Low Power Deep Learning on the OpenMV Cam
  • Tutorial: Low Power Deep Learning on the OpenMV Cam
  • Tutorial: Low Power Deep Learning on the OpenMV Cam
  • Tutorial: Low Power Deep Learning on the OpenMV Cam
  • Tutorial: Low Power Deep Learning on the OpenMV Cam
  • Tutorial: Low Power Deep Learning on the OpenMV Cam
  • Tutorial: Low Power Deep Learning on the OpenMV Cam
  • Tutorial: Low Power Deep Learning on the OpenMV Cam
  • Why Machine Learning on The Edge?
  • Tutorial: Low Power Deep Learning on the OpenMV Cam
  • Why Machine Learning on The Edge?
  • Tutorial: Low Power Deep Learning on the OpenMV Cam
  • Why Machine Learning on The Edge?
  • Tutorial: Low Power Deep Learning on the OpenMV Cam
  • Why Machine Learning on The Edge?
  • Tutorial: Low Power Deep Learning on the OpenMV Cam
  • Why Machine Learning on The Edge?
  • Tutorial: Low Power Deep Learning on the OpenMV Cam
  • Why Machine Learning on The Edge?
  • Tutorial: Low Power Deep Learning on the OpenMV Cam
  • Why Machine Learning on The Edge?
  • Tutorial: Low Power Deep Learning on the OpenMV Cam
  • Why Machine Learning on The Edge?
  • Tutorial: Low Power Deep Learning on the OpenMV Cam
  • Why Machine Learning on The Edge?
  • Tutorial: Low Power Deep Learning on the OpenMV Cam
  • Why Machine Learning on The Edge?
  • Tutorial: Low Power Deep Learning on the OpenMV Cam
  • Why Machine Learning on The Edge?
  • Tutorial: Low Power Deep Learning on the OpenMV Cam
  • Tutorial: Low Power Deep Learning on the OpenMV Cam
  • Tutorial: Low Power Deep Learning on the OpenMV Cam
  • Tutorial: Low Power Deep Learning on the OpenMV Cam
  • Tutorial: Low Power Deep Learning on the OpenMV Cam
  • Tutorial: Low Power Deep Learning on the OpenMV Cam
  • Tutorial: Low Power Deep Learning on the OpenMV Cam
  • Tutorial: Low Power Deep Learning on the OpenMV Cam
  • Beagleboard BeagleV - Open Source RISC-V-based Linux board that includes a Neural Network Engine.
  • Tutorial: Low Power Deep Learning on the OpenMV Cam
  • Tutorial: Low Power Deep Learning on the OpenMV Cam
  • Tutorial: Low Power Deep Learning on the OpenMV Cam
  • Tutorial: Low Power Deep Learning on the OpenMV Cam
  • Tutorial: Low Power Deep Learning on the OpenMV Cam
  • Tutorial: Low Power Deep Learning on the OpenMV Cam
  • Tutorial: Low Power Deep Learning on the OpenMV Cam
  • OpenMV - A camera that runs with MicroPython on ARM Cortex M6/M7 and great support for computer vision algorithms. Now with [support for Tensorflow Lite too](https://openmv.io/blogs/news/tensorflow-lite-and-person-detection).
  • Embedded Learning Library (ELL) - Microsoft's library to deploy intelligent machine-learned models onto resource constrained platforms and small single-board computers.
  • uTensor - AI inference library based on mbed (an RTOS for ARM chipsets) and TensorFlow.
  • Neural Network on Microcontroller (NNoM) - Higher-level layer-based Neural Network library specifically for microcontrollers. Support for CMSIS-NN.
  • nncase - Open deep learning compiler stack for Kendryte K210 AI accelerator.
  • Hardware benchmark for edge AI on cubesats - Open Source Cubesat Workshop 2018
  • Tutorial: Low Power Deep Learning on the OpenMV Cam
  • TinyML Papers and Projects - Compilation of the most recent paper's and projects in the TinyML/EdgeAI field.
  • Tutorial: Low Power Deep Learning on the OpenMV Cam
  • Tutorial: Low Power Deep Learning on the OpenMV Cam
  • JeVois - A TensorFlow-enabled camera module.
  • NVIDIA Jetson - High-performance embedded system-on-module to unlock deep learning, computer vision, GPU computing, and graphics in network-constrained environments.
  • M5StickV - AIoT(AI+IoT) Camera powered by Kendryte K210
  • GreenWaves GAP8 - RISC-V-based chip with hardware acceleration for convolutional operations.
  • GreenWaves GAP9 - RISC-V-based chip primarily focused on AI-centric audio processing.
  • STM32N6 - Arm Cortex-M55 running at 800MHz that embeds an neural processing unit (NPU).
  • Grove Vision AI Module V2 - Arm Cortex-M55 and Ethos U-55 neural processing unit (NPU).
  • Arduino Nicla Voice - Soc based on Arm Cortex-M4 nRF52832 and includes the Syntiant NDP120 Neural Decision Processor
  • LiteRT
  • TensorFlow Lite for Microcontrollers - Port of TF Lite for microcontrollers and other devices with only kilobytes of memory. Born from a [merge with uTensor](https://os.mbed.com/blog/entry/uTensor-and-Tensor-Flow-Announcement/).
  • Qeexo AutoML - Interactive platform to generate AI models targetted to microcontrollers.
  • AIfES - platform-independent and standalone AI software framework optimized for embedded systems.
  • onnx2c - ONNX to C compiler targeting "Tiny ML".
  • Imagimob - They offer a toolset (DEEPCRAFT) aimed at developing ML models for embedded devices.
  • emlearn - ML inference engine for microcontrollers and embedded devices.
  • Z-Ant - SDK for deploying optimized NNs on microcontrollers.
  • micro:bit CreateAI - Web-based tool to train an ML model and then run it on a BBC micro:bit V2.
  • AutoML VS Code Extension - Open Source VSCode Extension for training, optimizing and deploying tailored models on Edge platforms using AutoML (uses [Kenning ML framework](https://github.com/antmicro/kenning)).
  • tinyML Summit - Annual conference and monthly meetup celebrated in California, USA. Talks and slides are usually [available from the website](https://www.tinymlsummit.org/#meetups).
  • MinUn - Accurate ML Inference on Microcontrollers.
  • Edge AI Engineering Online Book - Textbook for IESTI05 Edge AI Engineering at the Federal University of Itajubá (UNIFEI) in Brazil focused primarily on the Raspberry Pi.
  • How AI on Microcontrollers Actually Works: Operators and Kernels
  • NXP DNPUs - Discrete Neural Processing Units (DNPUs) hardware accelerators (Kinara aquisition).
  • Benchmarking Edge Computing (May 2019)
  • TensorFlow Lite - Lightweight solution for mobile and embedded devices which enables on-device machine learning inference with low latency and a small binary size.
  • Raspberry Pi AI HAT+ - Hailo-8 or Hailo-8L accelerator offering 26 TOPS or 13 TOPS inferencing performance respectively.
  • TinyML: Machine Learning with TensorFlow on Arduino and Ultra-Low Power Micro-Controllers - O'Reilly book written by Pete Warden, Daniel Situnayake.
  • Nordic's nRF54L Series - Low-power SoC based on an Arm Cortex-M33 with BLE/Matter/Thread/Zigbee connectivity and an Axon NPU.
  • Why Machine Learning on The Edge?
  • Syntiant TinyML - Development kit based on the Syntiant NDP101 Neural Decision Processor and a SAMD21 Cortex-M0+.
  • Eclipse Aidge - Open-source framework for optimizing and deploying deep neural networks on embedded systems.
  • Syntiant TinyML - Development kit based on the Syntiant NDP101 Neural Decision Processor and a SAMD21 Cortex-M0+.
  • edge-agents (ForestHub) - Open-source (AGPL-3.0) 30 MB AI agent runtime for edge devices. Offline by default; GPIO/UART/MQTT as first-class nodes; local SLMs alongside cloud LLMs. Runs on Raspberry Pi 5, Jetson Orin Nano, STM32MP25, ctrlX CORE.
  • TiGrIS - Ahead-of-time compiler that tiles ONNX models to fit microcontrollers with fixed RAM and flash budgets, executed by a dependency-free C runtime.