I’m working on a project with Florian that involves using nengo solvers and a lot of sample points. When we start getting up around a million sample points, memory usage seems to explode. For example:
import nengo import numpy as np N = 750 D1 = 16 D2 = 2 S = 1000000 pts = np.zeros((S, D1)) target = np.zeros((S, D2)) pts[:,0] = np.sin(np.arange(S)*0.001) target[:,0] = np.sin(np.arange(S)*0.001) model = nengo.Network() with model: ens = nengo.Ensemble(n_neurons=N, dimensions=D1) result = nengo.Node(None, size_in=D2) c = nengo.Connection(ens, result, eval_points=pts, function=target) sim = nengo.Simulator(model)
Any suggestions? Is there an alternate solver that would be better in this situation?