1. PANN Testing

2. Training Time and Error Benchmarking
Target error: (Mean Squared Error)

3. Training Time and Error Benchmarking
Progress ANN reduces its training error to desired minimum in less than one second.
Perceptron and Back Propagation ANNs have substantial error, which reduces slowly. Perceptron and Back Propagation ANNs show no tendency to reach target error.
Tested training set:
30,000 images 60
Perceptron 50 40
Mean Squared Error (x 100) 30
Back propagation 20
Progress ANN 10
Target error: 0.02 50
250
1250
6250
31250
156250
781250
Time (milliseconds)

4. Progress P-Net Creator NeuroSolution Data Manager
Additional Comparison to Alternatives
Progress P-network
NeuroSolutions
Parameters
Progress P-network
NeuroSolution Data Manager
Standard deviation
0.0035
0.011
Working time
3.297 sec
1938 sec = 32 minutes 18 sec
Number of epochs 8
44000
Progress P-Net Creator
NeuroSolution Data Manager

5. Training advantage factor =
Additional Comparison to Alternatives
Number of: records, images, lines, samples, data set and sample data are used synonymously.
Comparison of training time between IBM SPSS Statistics 22 and P-network, for the same problem, tested on Apple iMac 27" 3.5 GHz quad-core Intel Core i7 8GB of 1600MHz DDR3 memory; SSD
14000
IBM SPSS Statistics 22
Exponential growth of training time
12000
10000
Network
Images
Training time
Progress
P-network
7000
4 sec.
IBM SPSS Statistic 22
13400 =
3h. 43min.
8000
Time in seconds
6000
4000
2000
𝟏𝟑𝟒𝟎𝟎 𝒔𝒆𝒄 𝟒 𝒔𝒆𝒄 =𝟑𝟑𝟓𝟎
Training advantage factor =
Number of images
1000
2000
3000
4000
5000
6000
7000 4
Progress P-network
Linear growth of training time 3
Time in seconds 2 1
Number of images
1000
2000
3000
4000
5000
6000
7000
с Progress, Inc. 2013

6. PANN vs. Classical Neural Network
Theoretically unlimited intelligence
Long training time
PANN
Practically unlimited intelligence
Quick training
Human brain
Training Time 10
1013
1012
1011
1010
109
107
108
106
105
104
103
102
Classical neural network
PANN Hardware
1 hour
1 minute
24 hour
1 month
1 year
1 million years
1 thousand years
Network Intelligence
1014
Network Intelligence is proportional to the number of elements and problems at hand
Seconds
PANN Software
PANN requires minutes (at most - hours) to reach the level of network intelligence unreachable for classical neural networks for thousands of years
с Progress, Inc. 2013

7. Test on images compression Files with images from CIFAR-10 test site
No compression
20000
MSE:0.013
MSE:0.006
15000
MSE:0.005
MSE:0.011
Economy of disk space
Number of color images in a file
10000
MSE:0.008
MSE:0.004
PANN with 50 weights on a synapse
PANN with 100 weights on a synapse
5000
1000
100 1 50
100
150
200
250
300
File size on disk, MB

8. #2 GPUs breakthrough - 2 Amdahl’s Law and P-net
PANN simple matrix algebra mathematics allows 100% parallel processing. Thus, speed increases linearly with additional GPUs and CPUs.
Progress US patent application 15/449,614 that covers matrix algebra application with PANN, is filed on March 3, 2017.
P-net application
This allows building computers and other electronics with:
very high processing speed, and
reduced number of GPUs and CPUs

9. Trading speed: Comparison of PANN and nVidia cuDNN
#2 GPUs breakthrough - 2
Trading speed: Comparison of PANN and nVidia cuDNN
PANN training speed on CPU and GPUs is thousand times higher than of existing ANN.
Allows to:
Improve ANN training speed thousands times
Build supercomputer on GPUs
Build Hypercomputer on GPUs
PANN provides 60 (threads on GPU) x = x.
Acceleration is proportional to the number (N) of GPUs:
x N

10. Comparison of P-net with CPU/GPU
Training time, in msec
inputs
outputs
images
CPU
GPU
Log CPU time
Log GPU time
difference
times
10.00 -
3.00
100.00
2.00
3.10
0.30
0.49
0.65
321.00
19.90
2.51
1.30
301.10
16.13
271.00
3.90
2.43
29.05
CPU - central processing unit
GPU - graphics processing unit
1 sec = 1000 msec
Testing computer with CPU speed/ GPU speed = 4
time, log

11. Comparison of P-net with CPU/GPU
training time, in msec
inputs
outputs
images
CPU, ms
GPU
log CPU time
log GPU time
difference
times
100.00
10.00
2.00
69.10
0.30
1.84
0.03
28.00
69.30
1.45
0.40
86.40
3.57
1.94
43.04
125.30
4.27
2.10
147.17
CPU - central processing unit
GPU - graphics processing unit
time, log