multiplied 0.10.0

A powerful tool to build, test, and analyse multiplier designs.

Introduction

Multiplied is a library for exploring and quickly defining combinational multiplication algorithms. The library also bundles built-in tools to analyse and visualise algorithms through Pandas and Matplotlib.

The Problem

Generating and analysing multiplier designs by hand is labour intensive, even for small datasets, for entire truth tables it's close to impossible.

Multiplied is built to streamline this process:

• Custom partial product reduction via templates
• Generating complete truth tables
• Analysis, plotting, and managing datasets
• Fine-grain access to bits, words or stages

Pattern Based Algorithm

Multiplied uses Algorithm objects to store each stage of reduction. Each of which is made up of a Template, pseudo Matrix, and a Map.

• Patterns represent simple templates
• Automatic mapping based on empty rows
• "Pseudo" matrix to visualise possible bit positions for arithmetic outputs.

p = mp.Pattern(['a','a','b','b','c','c','d','d'])
alg = mp.Algorithm(8)
alg.push(p)
print(alg)

0:{

template:{

________AaAaAaAa
_______aAaAaAaA_
______BbBbBbBb__
_____bBbBbBbB___
____CcCcCcCc____
___cCcCcCcC_____
__DdDdDdDd______
_dDdDdDdD_______

______AaAaAaAaAa
________________
____BbBbBbBbBb__
________________
__CcCcCcCcCc____
________________
DdDdDdDdDd______
________________
}

pseudo:{

______AaAaAaAaAa
____BbBbBbBbBb__
__CcCcCcCcCc____
DdDdDdDdDd______
________________
________________
________________
________________
}

map:{

00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00
00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00
FF FF FF FF FF FF FF FF FF FF FF FF FF FF FF FF
00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00
FE FE FE FE FE FE FE FE FE FE FE FE FE FE FE FE
00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00
FD FD FD FD FD FD FD FD FD FD FD FD FD FD FD FD
00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00
}

Automatic Template Generation

Extend the previous single stage, pattern based algorithm using auto resolution:

alg.auto_resolve_stage(recursive=True)

Algorithm Execution

With the algorithm object complete, you can execute it with the following code:

result = alg.exec(42, 255)

for m in result.values():
    print(m)

# convert result to decimal
print(int("".join(alg.matrix.matrix[0]), 2))
print(a*b)

________00101010
_______00101010_
______00101010__
_____00101010___
____00101010____
___00101010_____
__00101010______
_00101010_______

______0011010110
______00010100__
___0011010110___
___00010100_____
0001111110______
________________
________________
________________

___0011000110110
_____00110100___
00100010000_____
________________
________________
________________
________________
________________

0010010110010110
__0001000100____
________________
________________
________________
________________
________________
________________

0010100111010110
________________
________________
________________
________________
________________
________________
________________

10710
10710

Analysis

Generated data returns as a Pandas DataFrame ready for manipulation and visualisation:

import pandas as pd

domain_ = (1, 255)  # range of possible operand values for a and b
range_ = (1, 65535)  # range of possible output values
scope = mp.truth_scope(domain_, range_)  # generator clamps range to domain

# scope yields input tuples (a, b) to generate a Pandas DataFrame
df = mp.truth_dataframe(scope, alg)

# Generate cumulative heatmap of all stages
mp.df_global_heatmap("example.svg", "Title", df, dark=True)

# Generate and stack 2d heatmaps of each stage
mp.df_global_3d_heatmap("example3d.svg", "Title", df, dark=True)