Inputs and Outputs of Placement
Inputs to Placement Netlist/Verilog(.V) The Logical Connectivity of all cells in design is required here in placement. So, the Netlist requirement is of higher priority. Floorplan DEF(Design Exchange Format) icc_shell> write_def —> will store database till Floorplan. icc_shell> read_def when required. The most requirement would be when there is a bad floorplan found in the…
Checks before Placement
Before starting placement it is better to do some analysis and checks on design/tool settings. This helps in reducing iterations. Check for missing or extra placement and routing blockages(Hard/Soft/Partial). Try using partial blockages mostly as they take some ratio of inverters/buffers while doing optimization. Hard blockages don’t allow any optimization cells, so it would be…
Placement Objectives & Quality Checks
Few of the objectives have to be maintained to achieve proper placement of cells in the design without degrading their functionality. Performance(Timing): In the placement stage, we only have placement of cells information but not their connectivity information. Routing is done in a way that it does not take much time for signal transition giving…
Time module
Python offers a module called ‘time’ to create varying functions that are related to time as well as to achieve time-related tasks. Consider a function from the ‘time’ module which returns the number of seconds passed since ‘epoch’. Epoch is the time passed since January the first of 1970. import time # This prints time…
datetime module
Python does not have a direct implementation to work with dates and time but we can use a built-in module called “datetime” to work with dates and time as objects. Consider an example: import datetime current_date_time = datetime.datetime.now() print(current_date_time) When we run this, we get: The output is displayed in the format of Year-Month-Day Hour:Minute:Second.Microsecond. Each…
Shallow and Deep Copy
In Python, we can use the ‘=’ operator to copy one value of a variable to another. However, when we copy a value what happens underneath is that Python is simply creating another reference to the same variable. When one reference changes its internal value, the change is reflected in all references. Another important aspect…
Garbage Collection
Garbage Collection Python uses Garbage Collection to free up memory that was given to the program after it has completed execution, and this also includes memory that was once given to a program at a certain time but is no longer in use by the program. Garbage collection helps us in memory management and also…
Assert Statement
Assert Statement Python offers a keyword known as the ‘assert’ keyword and we use it to run conditional checks to handle different errors. If the condition is not satisfied an error is raised and the program halts. Assert also tells us where the error happened. This condition is known as the ‘Assertion Condition’ and the…
Decorator & Property
Decorators To better understand Decorators, consider a scenario of painting a house. If the house is already painted in one color let’s say white, we also have the option of adding another color of paint to a specific part of the house, without having to remove the entire paint of the house. Likewise, Decorators give…
Generators & Clousers
Generators Generators are functions and a function is deemed as a Generator function if it has one or more yield statements. Generators also provide us with a very simplistic way of creating iterator objects. They also do not store all of the values instead they generate one value at a time. In a practical setting,…
