Update submodule
git submodule update --remote
Build in ARM-Linux
./build.py
CPU accessing GPU memory takes about 4 times slower (#4)
Backpropagation Neural Network with QPULib
License: Other
Update submodule
git submodule update --remote
Build in ARM-Linux
./build.py
CPU accessing GPU memory takes about 4 times slower (#4)
for (int n = 0; n < neurons_; n++)
{
output = (*(outVector_))[n];
Does this take take or should I use a float pointer?
Target: Network with 2 input 1, one hidden layer with 3 neurons, learning XOR logic.
We can run the network with 4 cpu cores and 1 gpu core.
There can be loads if we use copy from heap to gpu mem and back.
Gather informations to decide how the memory configuration should be.
Use float
for storing values.(4 bytes)
Case of mnist,
About 200MB used
Edit /boot/config.txt
## gpu_mem
## GPU memory allocation in MB for all board revisions.
##
## Default 64
##
gpu_mem=448
Change amount of gpu_mem
value
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