Spires feeding into SPICE crossbar

1 files changed, 0 insertions, 151 deletions

diff --git a/src/torch_reservoir.py b/src/torch_reservoir.py
deleted file mode 100644
index a21ed75..0000000
--- a/src/torch_reservoir.py
+++ /dev/null
@@ -1,151 +0,0 @@
-import numpy as np
-import torch 
-import memtorch
-from spires_interface import (
-        free_spires_reservoir,
-        init_spires_reservoir,
-        send_currents_to_spires,
-        read_spikes_from_spires,
-)
-import matplotlib.pyplot as plt
-from sklearn.linear_model import Ridge
-import time
-
-# ----- PARAMETERS -----
-NUM_INPUTS = 1
-NUM_OUTPUTS = 1
-NUM_NEURONS = 800
-time_steps = 2000
-
-input_layer = torch.nn.Linear(NUM_INPUTS, NUM_NEURONS, bias=False)
-#this is a recurrent layer sort of???
-reservoir_layer = torch.nn.Linear(NUM_NEURONS, NUM_NEURONS, bias=False)
-readout_layer = torch.nn.Linear(NUM_NEURONS, NUM_OUTPUTS, bias=False)
-
-spires_reservoir = init_spires_reservoir(NUM_NEURONS)
-
-#make the reservoir sparse and random
-with torch.no_grad():
-    reservoir_layer.weight.data.normal_(0.0, 0.5) #random weight
-
-    #mask so 10% of connections exist
-    mask = (torch.rand(NUM_NEURONS, NUM_NEURONS) < 0.10).float()
-    reservoir_layer.weight.data *= mask
-
-# patch layers into memristor crossbars with memTorch
-memristor_model = memtorch.bh.memristor.VTEAM
-memristor_model_params = {
-        'time_series_resolution': 1e-10,
-        'r_on': 50,
-        'r_off': 1000,
-}
-
-mem_input_layer = memtorch.mn.Module.patch_model(
-        input_layer, 
-        memristor_model,
-        memristor_model_params,
-)
-
-mem_reservoir_layer = memtorch.mn.Module.patch_model(
-        reservoir_layer,
-        memristor_model,
-        memristor_model_params,
-)
-
-start_time = time.perf_counter()
-
-# ----- run spires & collect spikes-----
-previous_spikes = torch.zeros(1, NUM_NEURONS)
-spike_history = []
-
-input_signal = []
-t = np.linspace(0, 20, time_steps)
-
-input_signal = np.sin(-2.5 * t) + np.sin(5 * t)
-target_signal = np.roll(input_signal, -5) #predicting 5 time steps in the future
-
-for step in range(time_steps):
-
-    input_tensor = torch.tensor([input_signal[step]]).float().unsqueeze(0)
-
-    currents_in = mem_input_layer(input_tensor)
-    currents_recv = mem_reservoir_layer(previous_spikes)
-
-    noise_multiplier = 0.08
-    current_scaler = 0.05
-
-    total_currents = (currents_in + currents_recv)
-    total_currents = total_currents - total_currents.mean() # remove DC bias
-    total_currents = total_currents + (torch.randn_like(total_currents) * noise_multiplier )
-    total_currents = total_currents * current_scaler #scale to fit threshhold
-
-    send_currents_to_spires(spires_reservoir, total_currents)
-    current_spikes = read_spikes_from_spires(spires_reservoir, NUM_NEURONS)
-
-    spike_history.append(current_spikes.copy())
-
-    # convert spikes back to tensor
-    previous_spikes = torch.from_numpy(current_spikes).float().unsqueeze(0)
-    
-#low pass filter
-spike_matrix = np.array(spike_history)
-
-decay_rate = 0.85
-filtered_spikes = np.zeros_like(spike_matrix, dtype=float)
-
-current_trace = np.zeros(NUM_NEURONS)
-for i in range(len(spike_matrix)):
-    current_trace = current_trace * decay_rate + spike_matrix[i]
-    filtered_spikes[i] = current_trace
-
-#training readout layer
-print("training the readout layer")
-x_train = filtered_spikes[100:800]
-y_train = target_signal[100:800]
-
-x_test = filtered_spikes[800:]
-y_test = target_signal[800:]
-
-ridge = Ridge(alpha=5)
-ridge.fit(x_train, y_train)
-
-#predict and plot results
-print("Generating predictions...")
-predictions = ridge.predict(x_test)
-
-end_time = time.perf_counter()
-execution_time = end_time - start_time
-print(f"execution time: {execution_time}")
-
-test_steps = np.arange(800, time_steps)
-
-plt.figure(figsize=(10, 5))
-plt.plot(test_steps, y_test, label="True Future Wave", color="black", linestyle="dashed")
-plt.plot(test_steps, predictions, label="Reservoir Prediction", color="blue", alpha=0.8)
-plt.title("spires memristor Time-Series prediction")
-plt.xlabel("time steps")
-plt.ylabel("Amplitude")
-plt.legend()
-plt.tight_layout()
-plt.savefig("memristor_spires_proof.png")
-
-free_spires_reservoir(spires_reservoir)
-
-# # ---------- Plotting ----------
-# #This block is all AI generated to be transparent
-# #plotting to verify neurons are firing randomly
-#
-# print("Simulation complete, plotting results") #why? cuz its fun and almost 5
-# spike_matrix = np.array(spike_history)
-# # Plotting
-# plt.figure(figsize=(12, 6))
-# # Transpose (.T) so Time is the X-axis and Neurons are the Y-axis
-# plt.imshow(spike_matrix.T, aspect='auto', cmap='binary', interpolation='nearest')
-#
-# plt.title("Reservoir Spiking Activity (Raster Plot)")
-# plt.xlabel("Time Step")
-# plt.ylabel("Neuron ID (0 to 799)")
-# plt.colorbar(label="Spike (0 or 1)")
-# plt.tight_layout()
-# plt.show()
-#