edge-ai

A camera that runs with MicroPython on ARM Cortex M6/M7 and great support for computer vision algorithms. Now with

A TensorFlow-enabled camera module

Google’s purpose-built ASIC designed to run inference at the edge

Intel's family of SoCs designed specifically for low power on-device computer vision and neural network applications

Line of products based on Intel Movidius VPUs (including Myriad 2 and Myriad X) and Intel Cyclone FPGAs

An embedded platform for combining Depth and AI, built around Myriad X

High-performance embedded system-on-module to unlock deep learning, computer vision, GPU computing, and graphics in network-constrained environments

High-performance SDR seamlessly integrated with state-of-the-art deep learning hardware

Dual-core, RISC-V chip with convolutional neural network acceleration using 64 KLUs (Kendryte Arithmetic Logic Unit)

Based on the Kendryte K210, the module adds WiFi connectivity and an external flash memory

AIoT(AI+IoT) Camera powered by Kendryte K210

AI Camera powered by Kendryte K210 (lower-end M5StickV)

Tri-core RISC-V processor clocked with AI accelerators

RISC-V-based chip with hardware acceleration for convolutional operations

RISC-V-based chip primarily focused on AI-centric audio processing

Embedded development platform featuring a Xilinx UltraScale+ MPSoC FPGA

SparkFun Edge Development Board powered by a Cortex M4 from Ambiq Micro

Platform of hardware components and software tools for local AI products based on Google Edge TPU coprocessor

Family of chips optimized for edge computing

Processors designed to accelerate ML inference (being the first one the Ethos-U55)

SoC similar to the well-known ESP32 with support for AI acceleration (among many other interesting differences)

SoC based on a Cortex-M4 that includes a CNN accelerator

Open Source RISC-V-based Linux board that includes a Neural Network Engine

Development kit based on the Syntiant NDP101 Neural Decision Processor and a SAMD21 Cortex-M0+

Lightweight solution for mobile and embedded devices which enables on-device machine learning inference with low latency and a small binary size

Port of TF Lite for microcontrollers and other devices with only kilobytes of memory. Born from a

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

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

Set of optimized functions for image processing, computer vision, and machine learning

Libraries to developers run NN models on Snapdragon mobile platforms taking advantage of the CPU, GPU and/or DSP

Toolkit for generating NN optimiezed for STM32 MCUs

Tool that generates a model to be loaded into an STM32 MCU

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

deepC

Deep learning compiler and inference framework targeted to embedded platform

is an open source tool to optimize tensor programs

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

Interactive platform to generate AI models targetted to microcontrollers

C++ header-only fast machine learning library that focuses on lightweight deployment. It has a wide variety of machine learning algorithms with the possibility to realize on-device learning on MPUs

AIfES

platform-independent and standalone AI software framework optimized for embedded systems

onnx2c

ONNX to C compiler targeting "Tiny ML"

O'Reilly book written by Pete Warden, Daniel Situnayake

Annual conference and monthly meetup celebrated in California, USA. Talks and slides are usually

TinyML Papers and Projects

Compilation of the most recent paper's and projects in the TinyML/EdgeAI field

MinUn

Accurate ML Inference on Microcontrollers