8 files changed, 226 insertions, 5 deletions
diff --git a/.~lock.project description.24.doc# b/.~lock.project description.24.doc#
new file mode 100644
index 0000000..bed60cd
--- /dev/null
+++ b/.~lock.project description.24.doc#
@@ -0,0 +1 @@
+,indigo,indigo,11.04.2024 20:05,file:///home/indigo/.config/libreoffice/4;
+\ No newline at end of file
diff --git a/booth_multiplier.py b/booth_multiplier.py
index afd6710..dc32263 100755
--- a/booth_multiplier.py
+++ b/booth_multiplier.py
@@ -91,8 +91,12 @@ def booth_mod(multiplier, multiplicand, length):
   return (result, operations)
 
 if __name__ == "__main__":
-  headers = ['multiplicand', 'multiplier', 'result (bin)', 'result (hex)']
-  table = []
+  result_headers = ['multiplicand', 'multiplier', 'result (bin)', 'result (hex)']
+  result_table = []
+
+  opcount_headers = ['multiplicand', 'multiplier', 'length', 'booth', 'modified booth']
+  opcount_table = []
+
   lengths = [] # for matplotlib plot
   ops_booth = []
   ops_mod_booth = []
@@ -106,7 +110,7 @@ if __name__ == "__main__":
     multiplicand = int(operation.split(" ")[0], 2)
     multiplier = int(operation.split(" ")[1], 2)
 
-    
+    # get result and operation count of both algorithims
     result_booth = booth(multiplier, multiplicand, length)
     result_mod_booth = booth_mod(multiplier, multiplicand, length)
 
@@ -115,11 +119,19 @@ if __name__ == "__main__":
     ops_mod_booth.append(result_mod_booth[1])
     lengths.append(length)
     
-    table.append([bin(multiplicand), bin(multiplier), bin(result_booth[0]), hex(result_booth[0])])
+    #gather data for report results table
+    result_table.append([bin(multiplicand), bin(multiplier), bin(result_booth[0]), hex(result_booth[0])])
+
+    #gather data for test function to check if simulator is working
     debug_results.append([multiplicand, multiplier, result_booth[0], result_mod_booth[0], length])
 
+    #gather data for operation count table
+    opcount_table.append([bin(multiplicand), bin(multiplier), length, result_booth[1], result_mod_booth[1]])
+
   debug(debug_results)
-  print(tabulate(table, headers))
+  print(tabulate(result_table, result_headers))
+  print(tabulate(opcount_table, opcount_headers))
+
   
   # generate graph
   plt.plot(lengths, ops_booth, '^--m', label='booths algorithim')
diff --git a/pheudo_code b/pheudo_code
deleted file mode 100644
index e69de29..0000000
--- a/pheudo_code
+++ /dev/null
diff --git a/project description.24.doc b/project description.24.doc
new file mode 100644
index 0000000..21d4642
--- /dev/null
+++ b/project description.24.doc
diff --git a/report/report.aux b/report/report.aux
new file mode 100644
index 0000000..b640121
--- /dev/null
+++ b/report/report.aux
@@ -0,0 +1,2 @@
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new file mode 100644
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diff --git a/report/report.pdf b/report/report.pdf
new file mode 100644
index 0000000..899037f
--- /dev/null
+++ b/report/report.pdf
diff --git a/report/report.tex b/report/report.tex
new file mode 100644
index 0000000..581d2ba
--- /dev/null
+++ b/report/report.tex
@@ -0,0 +1,36 @@
+\documentclass[12pt]{article}
+\usepackage{geometry}
+\usepackage{titling}
+\usepackage{helvet}
+\usepackage{tgpagella} % text only
+
+
+\geometry{
+  a4paper,
+  lmargin=1in,
+  rmargin=1in,
+  tmargin=1in,
+  bmargin=1in,
+}
+\setlength{\droptitle}{-3em}   % This is your set screw
+
+
+
+
+\title{\fontfamily{phv}\selectfont
+Analyzing Performance of Booth’s Algorithm and Modified Booth’s Algorithm}
+\author{Brett Weiland}
+
+\begin{document}
+\maketitle
+\begin{abstract}
+In this paper, the performance of Booth’s Algorithm is compared to modified Booth's Algorithm. Each multiplier is simulated in Python, and performance is observed by counting the number of add and subtract operations for various inputs. Results are analyzed and discussed to highlight the tradeoffs between the two multipliers.
+\end{abstract}
+\section*{Introduction}
+Multiplication is among the most time consuming mathematical operations for processors. In many applications, the time it takes to multiply dramatically influences the speed of the program. Applications of digital signal processing (such as audio modification and image processing) require constant multiply and accumulate operations for functions such as fast fourier transformations and convolutions. Other applications are heavily dependent on multiplying large matrices, such as machine learning, 3D graphics and data analysis. In such scenarios, the speed of multiplication is vital. Consequently, most modern processors implement hardware multiplication. However, not all multiplication circuits are equal; there is often a stark contrast between performance and hardware complexity. To further complicate things, multiplication circuits perform differently depending on what numbers are being multiplied.
+\section*{Implementation}
+
+\section*{Results}
+\section*{Analysis}
+\section*{Conclusion}
+\end{document}