Update README.md

README.md

@@ -12,7 +12,8 @@ CPUs are known for their general purpose use, the same GPUs can power all kinds
 In this case we are going to use the FPGA to implement a processing unit in hardware from High Level C code that will be able to compute 
 1. The weight matrix of a neural network 
 or 
-2. Do a liner search 
+2. Do a liner search
+
 and 
 compare how CPU performs in comparision to our FPGA hardware that is exactly wired up to work on the kind of data we expect to provide as input.
 
@@ -30,8 +31,16 @@ Next we implement the same algorithm in our python code that will obviously run
 Then finally we can check the runtime and reach a conclusion on which is faster and why.
 
 # Tasks
-The Tasks adn time plan are recorded below:
+The Tasks and maximum estimated time:
+
+0. Problem statement and solution Plan brainstorming : 12 hrs
+1. Pseudo code and solution adjustment : 3 hrs
+2. Vivado study of other solutions, available tools, code and hardware correlation : 12 hrs
+3. Writting the code in Vivado : 3 hrs
+4. Implementing code and checking hardware features and making final adjustments : 5 hrs
+5. Bitstream generation python code for overlay : 2 hrs
+6. Implementing same algorithm in python CPU : 3 hrs
 
 
 # Resources and Future questions
-TO be added later
+To be added later