#css #cpu_monitoring #dashboard #java #linux #monitoring #monitoring_tool #server #spring #spring_boot #web

https://github.com/B-Software/Ward

#javascript #browser_detection #cpu_detection #device_detection #engine_detection #gpu_detection #javascript_library #jquery_plugin #os_detection #user_agent #user_agent_parser

https://github.com/faisalman/ua-parser-js

#swift #battery #bluetooth #cpu #disk #fans #gpu #macos #menubar #monitor #network #sensors #stats #temperature

https://github.com/exelban/stats

#other #control #cpu #curves #fan #fancontrol #gpu #pwm #speed #temperature

https://github.com/Rem0o/FanControl.Releases

#c_lang #amd #cpu_cache #cpu_monitoring #cpu_temperature #cpu_topology #cpu_voltage #cpuid #cpuinfo #epyc #intel #multi_core #overclocking #process_monitor #processor #processor_architecture #ram_info #ryzen #threadripper #timings #turbo_boost

https://github.com/cyring/CoreFreq

#java #cpu #deep_learning #docker #gpu #kubernetes #machine_learning #metrics #mlops #optimization #pytorch #serving

https://github.com/pytorch/serve

#java #cpu_usage #disk_utilization #hacktoberfest #hardware_information #jna #memory_usage #operating_system #process_list #processor #serialnumbers #system_monitoring #usb_devices

https://github.com/oshi/oshi

#swift #battery #bluetooth #clock #cpu #disk #fans #gpu #macos #menubar #monitor #network #sensors #stats #temperature

Stats is a tool that helps you monitor your macOS system from the menu bar. It shows you important information like CPU and GPU usage, memory and disk utilization, network activity, battery level, and more. You can install it manually or using Homebrew. Stats supports many languages and is efficient, though you can disable some modules to reduce energy impact. This tool is beneficial because it keeps you informed about your system's performance without needing to open multiple apps, helping you manage your computer better.

https://github.com/exelban/stats

#assembly #cpu #fpga #riscv #soc #softcore #spinalhdl #verilog #vhdl

This repository provides a highly configurable RISC-V CPU implementation written in SpinalHDL. Here are the key benefits and features The CPU can be customized with various plugins to add or remove features such as instruction and data caches, multiplication and division units, floating-point units, and more.
- **Performance** It includes a debug module that allows for Eclipse debugging via GDB, OpenOCD, and JTAG connections.
- **Compatibility** The CPU can be optimized for different FPGA targets, and it does not use any vendor-specific IP blocks.
- **Extensibility**: New instructions and peripherals can be added easily through the plugin system, making it highly extensible.

Overall, this implementation offers a flexible and powerful RISC-V CPU solution that can be tailored to various needs and applications.

https://github.com/SpinalHDL/VexRiscv

#c_lang #convolutional_neural_network #convolutional_neural_networks #cpu #inference #inference_optimization #matrix_multiplication #mobile_inference #multithreading #neural_network #neural_networks #simd

XNNPACK is a powerful tool that helps make neural networks run faster on various devices like smartphones, computers, and Raspberry Pi boards. It supports many different types of processors and operating systems, making it very versatile. XNNPACK doesn't work directly with users but instead helps other machine learning frameworks like TensorFlow Lite, PyTorch, and ONNX Runtime to perform better. This means your apps and programs that use these frameworks can run neural networks more quickly and efficiently, which is beneficial because it saves time and improves performance.

https://github.com/google/XNNPACK