# Machine learning to predict 8*8 matrix values using three independent matrices

Problem Statement

I have 4 main input features.

This is a small snippet of the data for clearer understanding.

Gate name -> for example AND Gate

index_1 -> [0.001169, 0.005416, 0.01391, 0.03037, 0.06381, 0.1307, 0.2645, 0.532]

index_2 -> [7.906e-05, 0.001123, 0.00321, 0.007253, 0.01547, 0.03191, 0.06478, 0.1305]

values -> [[11.0081, 14.0303, 18.8622, 27.3426, 43.8661, 76.7538, 142.591, 274.499], [11.3461, 14.3634, 19.1985, 27.6827, 44.2106, 77.0954, 142.926, 274.879], [12.258, 15.2816, 20.1095, 28.5856, 45.1057, 77.9778, 143.8, 275.758], [13.665, 16.7457, 21.5835, 30.0545, 46.5581, 79.4212, 145.252, 277.192], [15.6636, 18.9526, 23.9051, 32.4281, 48.9011, 81.7052, 147.477, 279.371], [17.8838, 21.5839, 26.8957, 35.7103, 52.3901, 85.2132, 150.89, 282.714], [19.3338, 23.6933, 29.7184, 39.1212, 56.4053, 89.9721, 155.913, 287.637], [18.7856, 23.9999, 31.1794, 41.7549, 60.0043, 95.0488, 162.951, 295.005]]

My task is to predict this values matrix, given that I have index_1 and index_2. Originally this values matrix is propagation delay, calculated using a simulator called SPICE.

Where I am facing problem

1. There is no written relation between Index_1, index_2 or values since simulator calculates this value using it's own models.

2. I have made a CSV file which contains the data in separate columns.

3. Another approach that I thought. If I can give index_1, index_2 and any 5*5 sub-matrix to the model, and the model can predict the values of whole 8*8 Matrix. But the problem is again, which machine learning model do I use.

Approaches Tried so Far

1. I have tried a CNN model for this but it is giving me very low accuracy.

2. Used one dense fully connected neural network but it is over-fitting the data and not giving me any values for matrix.

I am still stuck at how to predict the matrix values given this data. What are other strategies can be used?

• Have you tried Generative Networks such as GAN, you may be able to model this problem better with GAN. – Patel Sunil Sep 21 '18 at 4:30
• I did consider it but, it wont be able to predict the values of new matrix, it will simply remove the noise given after input matrix and try to recreate the original matrix. Correct me if i am wrong – Mrinal Mathur Sep 21 '18 at 5:08

3. Are the outputs independent? (e.g. having the (1,1) entry of the matrix, do I know something about the $$(i, j)$$ entry of it?