| # Copyright 2022 Ioces Pty. Ltd. | |
| # | |
| # Permission is hereby granted, free of charge, to any person obtaining a copy | |
| # of this software and associated documentation files (the "Software"), to deal | |
| # in the Software without restriction, including without limitation the rights | |
| # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell | |
| # copies of the Software, and to permit persons to whom the Software is | |
| # furnished to do so, subject to the following conditions: | |
| # | |
| # The above copyright notice and this permission notice shall be included in |
| import uvm_pkg :: *; | |
| class my_seq_item extends uvm_sequence_item; | |
| rand logic [7:0] addr; | |
| rand logic [7:0] data; | |
| constraint addr_range_cn { | |
| addr inside {[10:20]}; | |
| } | |
| constraint data_range_cn { |
| import sys,os | |
| import curses | |
| def draw_menu(stdscr): | |
| k = 0 | |
| cursor_x = 0 | |
| cursor_y = 0 | |
| # Clear and refresh the screen for a blank canvas | |
| stdscr.clear() |
In this article we compared different behavior of static, inline and static inline free functions in compiled binary.
All the following test was done under g++ 7.1.1 on Linux amd64, ELF64.
#pragma once
inline int only_inline() { return 42; }
static int only_static() { return 42; }
| import tensorflow as tf | |
| #aplly exponential decay on learning rate | |
| global_step = tf.Variable(0, trainable=False) | |
| stater_learning_rate = lr #for start | |
| learning_rate = tf.train.exponential_decay(starter_learning_rate, global_step, | |
| decay_steps, decay_rate, staircase=True) | |
| optimizer = tf.train.AdamOptimizer(learning_rate) |