// Mutate synapses.
void NetworkHomomorph::mutate()
{
int i, j, k, n;
float weight;
Synapse *synapse;
i = randomNeuron(true);
n = network->numNeurons;
for (j = 0; j < n; j++)
{
weight = (float)randomizer->RAND_INTERVAL(
synapseWeightsParm.minimum, synapseWeightsParm.maximum);
for (k = 0; k < (int)network->synapses[i][j].size(); k++)
{
synapse = network->synapses[i][j][k];
synapse->setWeight(weight);
}
}
}
int run(boost::mpi::environment& env, boost::scoped_ptr<nemo::Network>& net, unsigned ncount, unsigned scount,
unsigned duration, unsigned nType, unsigned mType, const char* outfile, const char* configFile, const char* logDir) {
boost::mpi::communicator world;
boost::filesystem::path logDirPath(logDir);
boost::filesystem::create_directory(logDirPath);
std::ostringstream simDir;
simDir << logDir << "/simulation-p" << world.size() << "-n" << ncount << "-s" << scount << "-nt" << nType << "-mt" << mType;
boost::filesystem::path simDirPath(simDir.str());
boost::filesystem::create_directory(simDirPath);
try {
if ( world.rank() == nemo::mpi::MASTER ) {
nemo::Configuration conf(world.rank(), configFile);
std::cout << "Constructing master..." << std::endl;
nemo::mpi::Master master(env, world, *net, mType, conf, logDir);
std::ostringstream filename;
filename << simDir.str() << "/" << outfile;
std::ofstream file(filename.str().c_str());
rng_t rng;
uirng_t randomNeuron(rng, boost::uniform_int<>(0, ncount - 1));
master.resetTimer();
nemo::Simulation::firing_stimulus firingStimulus(10);
for ( unsigned ms = 0; ms < duration; ++ms ) {
for ( unsigned i = 0; i < 10; i++ )
firingStimulus[i] = randomNeuron();
master.step(firingStimulus);
const std::vector<unsigned>& firing = master.readFiring();
file << ms << ": ";
std::copy(firing.begin(), firing.end(), std::ostream_iterator<unsigned>(file, " "));
file << std::endl;
}
std::ostringstream oss;
oss << "Simulated " << master.elapsedSimulation() << " ms wall clock time: " << master.elapsedWallclock() << "ms"
<< std::endl;
std::cout << oss.str() << std::endl;
/* Write general stats to simulation folder */
filename.str("");
filename.clear();
filename << simDir.str() << "/general-stats.txt";
writeStats(filename.str().c_str(), scount, master.elapsedSimulation(), master.elapsedWallclock(), *net, conf);
master.appendMpiStats(filename.str().c_str());
/* Write firing counters to simulation folder */
filename.str("");
filename.clear();
filename << simDir.str() << "/firing-counters.txt";
master.writeFiringCounters(filename.str().c_str());
}
else {
std::cout << "Starting worker " << world.rank() << "..." << std::endl;
nemo::Configuration conf(world.rank(), configFile);
if ( conf.stdpEnabled() ) {
std::cout << "STDP enabled" << std::endl;
setStandardStdpFunction(conf);
}
nemo::mpi::runWorker(env, world, conf, simDir.str().c_str());
}
}
catch (nemo::exception& e) {
std::cerr << world.rank() << ":" << e.what() << std::endl;
env.abort(e.errorNumber());
}
catch (boost::mpi::exception& e) {
std::cerr << world.rank() << ": " << e.what() << std::endl;
env.abort(-1);
}
return 0;
}
// Initialize synapse optimization.
void NetworkHomomorph::initOptimize(vector<vector<Synapse *> >& synapses,
vector<vector<float> >& permutations,
int synapseOptimizedPathLength)
{
int i, j, k, n, p, q, s;
bool forward;
float weight;
vector<pair<int, int> > visited;
Synapse *synapse;
vector<Synapse *> chemSynapses, elecSynapses;
vector<vector<float> > weightRanges;
vector<float> weightRange;
vector<float> permutation;
synapses.clear();
permutations.clear();
// Randomly select starting neuron with synapse.
i = randomNeuron();
n = network->numNeurons;
for (s = 0; s < n; s++)
{
if (i < network->numSensors)
{
forward = true;
}
else if ((i >= network->numSensors) &&
(i < (network->numSensors + network->numMotors)))
{
forward = false;
}
else
{
forward = randomizer->RAND_BOOL();
}
if (forward)
{
for (j = 0; j < n; j++)
{
if (network->synapses[i][j].size() > 0)
{
break;
}
}
if (j < n)
{
break;
}
}
else
{
for (j = 0; j < n; j++)
{
if (network->synapses[j][i].size() > 0)
{
break;
}
}
if (j < n)
{
break;
}
}
i++;
i = (i % n);
}
if (s == n)
{
return;
}
// Select synapse path.
for (j = 0; j < synapseOptimizedPathLength; j++)
{
k = randomizer->RAND_CHOICE(n);
for (s = 0; s < n; s++)
{
if (forward)
{
if (network->synapses[i][k].size() > 0)
{
for (p = 0, q = (int)visited.size(); p < q; p++)
{
if ((visited[p].first == i) && (visited[p].second == k))
{
break;
}
}
if (p == q)
{
visited.push_back(pair<int, int>(i, k));
chemSynapses.clear();
elecSynapses.clear();
for (p = 0, q = (int)network->synapses[i][k].size(); p < q; p++)
{
synapse = network->synapses[i][k][p];
if (synapse->type == Synapse::CHEMICAL)
{
chemSynapses.push_back(synapse);
}
else
{
elecSynapses.push_back(synapse);
}
}
if (chemSynapses.size() > 0)
{
synapses.push_back(chemSynapses);
}
if (elecSynapses.size() > 0)
{
synapses.push_back(elecSynapses);
}
if ((chemSynapses.size() > 0) || (elecSynapses.size() > 0))
{
i = k;
break;
}
}
}
}
else
{
if (network->synapses[k][i].size() > 0)
{
for (p = 0, q = (int)visited.size(); p < q; p++)
{
if ((visited[p].first == k) && (visited[p].second == i))
{
break;
}
}
if (p == q)
{
visited.push_back(pair<int, int>(k, i));
chemSynapses.clear();
elecSynapses.clear();
for (p = 0, q = (int)network->synapses[k][i].size(); p < q; p++)
{
synapse = network->synapses[k][i][p];
if (synapse->type == Synapse::CHEMICAL)
{
chemSynapses.push_back(synapse);
}
else
{
elecSynapses.push_back(synapse);
}
}
if (chemSynapses.size() > 0)
{
synapses.push_back(chemSynapses);
}
if (elecSynapses.size() > 0)
{
synapses.push_back(elecSynapses);
}
if ((chemSynapses.size() > 0) || (elecSynapses.size() > 0))
{
i = k;
break;
}
}
}
}
k++;
k = (k % n);
}
if (s == n)
{
break;
}
}
if (synapses.size() == 0)
{
return;
}
// Compose synapse weight permutations.
for (i = 0, j = (int)synapses.size(); i < j; i++)
{
weightRange.clear();
weight = synapses[i][0]->weight;
weightRange.push_back(weight);
if (synapseWeightsParm.maxDelta > 0.0f)
{
weight = synapses[i][0]->weight -
(float)randomizer->RAND_INTERVAL(0.0f, synapseWeightsParm.maxDelta);
if (weight < synapseWeightsParm.minimum)
{
weight = synapseWeightsParm.minimum;
}
}
weightRange.push_back(weight);
if (synapseWeightsParm.maxDelta > 0.0f)
{
weight = synapses[i][0]->weight +
(float)randomizer->RAND_INTERVAL(0.0f, synapseWeightsParm.maxDelta);
if (weight > synapseWeightsParm.maximum)
{
weight = synapseWeightsParm.maximum;
}
}
weightRange.push_back(weight);
weightRanges.push_back(weightRange);
}
permutation.resize((int)synapses.size());
permuteWeights(weightRanges, permutations, permutation, 0, (int)synapses.size() - 1);
}
long
benchmark(nemo::Simulation* sim, unsigned n, unsigned m,
boost::program_options::variables_map& vm, unsigned seconds)
{
unsigned stdpPeriod = vm["stdp-period"].as<unsigned>();
float stdpReward = vm["stdp-reward"].as<float>();
bool csv = vm.count("csv") != 0;
bool verbose = !csv;
bool provideFiringStimulus = vm.count("fstim") != 0;
bool provideCurrentStimulus = vm.count("istim") != 0;
bool vprobe = vm.count("vprobe") != 0;
const unsigned MS_PER_SECOND = 1000;
rng_t rng;
uirng_t randomNeuron(rng, boost::uniform_int<>(0, n-1));
sim->resetTimer();
unsigned t = 0;
float v = 0;
/* Run for a few seconds to warm up the network */
if(verbose)
std::cout << "Running simulation (warming up)...";
for(unsigned s=0; s < 5; ++s) {
for(unsigned ms = 0; ms < MS_PER_SECOND; ++ms, ++t) {
sim->step();
if(stdpPeriod && t % stdpPeriod == 0) {
sim->applyStdp(stdpReward);
}
}
}
if(verbose)
std::cout << "[" << sim->elapsedWallclock() << "ms elapsed]";
sim->resetTimer();
if(verbose) {
std::cout << std::endl;
std::cout << "Running simulation (gathering performance data)...";
}
nemo::Simulation::firing_stimulus firingStimulus;
nemo::Simulation::current_stimulus currentStimulus;
unsigned long nfired = 0;
for(unsigned s=0; s < seconds; ++s) {
if(verbose)
std::cout << s << " ";
for(unsigned ms=0; ms<1000; ++ms, ++t) {
if(provideFiringStimulus) {
firingStimulus.resize(1);
firingStimulus[0] = randomNeuron();
}
if(provideCurrentStimulus) {
currentStimulus.resize(1);
currentStimulus[0] = std::make_pair(randomNeuron(), 0.001f);
}
const std::vector<unsigned>& fired = sim->step(firingStimulus, currentStimulus);
nfired += fired.size();
if(vprobe) {
/* Just read a single value. The whole neuron population will be synced */
v = sim->getMembranePotential(0);
}
if(stdpPeriod && t % stdpPeriod == 0) {
sim->applyStdp(stdpReward);
}
}
}
long int elapsedWallclock = sim->elapsedWallclock();
if(verbose)
std::cout << "[" << elapsedWallclock << "ms elapsed]";
if(verbose)
std::cout << std::endl;
unsigned long narrivals = nfired * m;
double f = (double(nfired) / n) / double(seconds);
/* Throughput is measured in terms of the number of spike arrivals per
* wall-clock second */
unsigned long throughput = MS_PER_SECOND * narrivals / elapsedWallclock;
double speedup = double(seconds*MS_PER_SECOND)/elapsedWallclock;
if(verbose) {
std::cout << "Total firings: " << nfired << std::endl;
std::cout << "Avg. firing rate: " << f << "Hz\n";
std::cout << "Spike arrivals: " << narrivals << std::endl;
std::cout << "Approx. throughput: " << throughput/1000000
<< "Ma/s (million spike arrivals per second)\n";
std::cout << "Speedup wrt real-time: " << speedup << std::endl;
}
if(csv) {
std::string sep = ", ";
// raw data
std::cout << n << sep << m << sep << seconds*MS_PER_SECOND
<< sep << elapsedWallclock << sep << stdpPeriod << sep << nfired;
// derived data. Not strictly needed, at least if out-degree is fixed.
std::cout << sep << narrivals << sep << f << sep << speedup
<< sep << throughput/1000000 << std::endl;
}
return elapsedWallclock;
}
