library

RV32E FLOATING POINT LIBRARY OFFERS 72 CODE SIZE REDUCTION

RV32E FLOATING-POINT LIBRARY OFFERS 72% CODE SIZE REDUCTION

SEGGER announces a new version of the RISC-V Floating-Point Library with full support for RV32E – the embedded variant of the RISC-V core. The new library leads to a massive reduction in code size for RISC-V applications using floating point. With all arithmetic functions hand-coded in assembly language, the memory footprint of RISC-V applications using floating-point […]

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CREATE MANUFACTURING READY FOOTPRINTS 3D MODELS WITH AUTODESK LIBRARY.IO

CREATE MANUFACTURING-READY FOOTPRINTS & 3D MODELS WITH AUTODESK LIBRARY.IO

If you ever made a PCB, you quickly realized that the component library of the PCB design software is usually pretty limited for such sophisticated programs. Usually, one has to resort to external tools to access the schematic symbols, footprints and 3D models, or design our own in the designer, which takes some precious time and is

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PSPICE FOR TI TOOL OFFERS SYSTEM LEVEL CIRCUIT SIMULATION

PSPICE FOR TI TOOL OFFERS SYSTEM-LEVEL CIRCUIT SIMULATION

Texas Instruments (TI) has announced a new custom version of the PSpice simulator from Cadence Design Systems that enables engineers to simulate complex analog circuits with unlimited analysis of TI power and signal-chain products. By Rich Pell @ smart2zero.com Available for download at no cost, PSpice for TI offers full-featured circuit simulation with a growing library of more

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Raspberry Pi Pico I2C Examples

Raspberry Pi Pico I2C Examples

The following short Python programs will demonstrate essential operation of the Raspberry Pi Pico board. These assume one or more I2C (or I2C) devices are externally attached. The I2C bus is a two-wire bidirectional serial bus for short-distance low-bandwidth communication between a microcontroller and peripherals. Each sample can be run by copying the program into code.py on

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Sample Model

Raspberry Pi Pico Decision Tree Classifier

This sketch demonstrates an example of accelerometer data processing using a classification tree. The classifier code was generated using the Python script classifier_gen.py using recorded and labeled training data. The underlying classification tree was automatically generated using the Python scikit-learn library. The purpose of the classifier is to categorize a multi-dimensional data point into an integer representing membership within

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