RSS Bot Posted August 17, 2022 Posted August 17, 2022 Sipeed TinyMaix open-source machine learning library is designed for microcontrollers, and lightweight enough to run on a Microchip ATmega328 MCU found in the Arduino UNO board and its many clones. Developed during a weekend hackathon, the core code of TinyMax is about 400 lines long, with a binary size of about 3KB, and low RAM usage, enabling it to run the MNIST handwritten digit classification on an ATmega320 MCU with just 2KB SRAM and 32KB flash. TinyMax highlights Small footprint Core code is less than 400 lines (tm_layers.c+tm_model.c+arch_O0.h), code .text section less than 3KB Low RAM consumption, with the MNIST classification running on less than 1KB RAM Support INT8/FP32 model, convert from keras h5 or tflite. Support multi-architecture acceleration: ARM SIMD/NEON, MVEI, RV32P, RV64V (32-bit & 64-bit RISC-V vector extensions) User-friendly interfaces, just load/run models Supports full static memory config MaixHub Online Model Training support coming soon Sipeed says there [...] The post TinyMaix is a lightweight machine learning library for microcontrollers appeared first on CNX Software - Embedded Systems News. View the full article
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