7 files changed, 90 insertions, 7 deletions

diff --git a/custom_benchmark/README.md b/custom_benchmark/README.md
deleted file mode 100644
index 7513f7f..0000000
--- a/custom_benchmark/README.md
+++ /dev/null
@@ -1,5 +0,0 @@
-# custom benchmark for memTorch simulations
-
-## metrics
-
-## tasks
diff --git a/custom_benchmark/memristor_benchmark.cpp b/custom_benchmark/memristor_benchmark.cpp
deleted file mode 100644
index 91e7eb6..0000000
--- a/custom_benchmark/memristor_benchmark.cpp
+++ /dev/null
@@ -1,2 +0,0 @@
-#include <iostream>
-#include <torch/extension.h>
diff --git a/quickstart b/quickstart
deleted file mode 100755
index 1c49674..0000000
--- a/quickstart
+++ /dev/null
diff --git a/spires_memristor_sim/README.md b/spires_memristor_sim/README.md
new file mode 100644
index 0000000..bc89498
--- /dev/null
+++ b/spires_memristor_sim/README.md
@@ -0,0 +1,12 @@
+# Spires reservoir simulated on memristor crossbar using memTorch
+
+## Goal
+The goal is to able to simulate a spires reservoir on a memristor crossbar using
+a custom memristor spice model(HIL), thus you can run multiple simulations with 
+multiple models to compare how each performs using benchmark results.
+
+## task
+The plan right now is to give the cart-pole task for benchmarking.
+
+## metrics
+
diff --git a/custom_benchmark/memTorch_SNN.py b/spires_memristor_sim/memTorch_SNN.py
index 67e9cb6..67e9cb6 100644
--- a/custom_benchmark/memTorch_SNN.py
+++ b/spires_memristor_sim/memTorch_SNN.py
diff --git a/spires_memristor_sim/spires_interface.py b/spires_memristor_sim/spires_interface.py
new file mode 100644
index 0000000..3f1e302
--- /dev/null
+++ b/spires_memristor_sim/spires_interface.py
@@ -0,0 +1,35 @@
+import ctypes
+import numpy
+import torch
+
+spires_lib = ctypes.CDLL("../spires/build/.libspires.so")
+
+spires_lib.spires_reservoir_step.argtypes = [ctypes.POINTER(ctypes.c_float)]
+spires_lib.spires_reservoir_step.restype = None
+
+#change currents from tensor to a flat C pointer array for spires to read
+def send_currents_to_spires(currents):
+    # ----- extract from pytorhc graph -----
+    # .detach() removes it from auto gradient tracking
+    # .cpu() make sure data is in RAM, not VRAM
+    # .numpy() maps it to numpy array
+    numpy_array = torch_tensor.detach().cpu().numpy()
+
+    # ----- makes suren layout matches 32 bit float C-array -----
+    # .astype(npfloat32) forces standard CC float precistion
+    # .flatten() makes sure the memory is a 1D block
+    contiguous_array = numpy_array.astype(np.float32).flatten()
+
+    # ----- Ensure raw memory pointer -----
+    c_float_ptr = contiguous_array.ctypes.data_as(ctypes.POINTER(ctypes.c_float))
+    array_size = contiguous_array.size
+
+    # ----- Call the C library -----
+    spires_lib.spires_reservoir_step(c_float_ptr, array_size)
+    return contiguous_array
+
+    # NEED TO MODIFY SPIRES FOR THIS?? :((
+
+def read_spikes_from_spires():
+    placeholder = 0
+    return 0
diff --git a/spires_memristor_sim/torch_reservoir.py b/spires_memristor_sim/torch_reservoir.py
new file mode 100644
index 0000000..c3017c5
--- /dev/null
+++ b/spires_memristor_sim/torch_reservoir.py
@@ -0,0 +1,43 @@
+import torch 
+import memtorch
+from spires_interface import (
+        send_currents_to_spires,
+        read_spikes_from_spires,
+)
+
+NUM_INPUTS = 4
+NUM_NEURONS = 800
+
+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)
+
+#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
+mem_input_layer = memtorch.mn.Module.patch_model(input_layer, memristor_model)
+mem_reservoir_layer = memtorch.mn.Module.patch_model(reservoir_layer, memristor_model)
+
+#run spiking loop with spires
+previous_spikes = torch.zeros(1, NUM_NEURONS)
+for step in range(500):
+    input_tensor = torch.tensor(cartpole_state).float().unsqueeze(0)
+    currents_in = mem_input_layer(input_tensor)
+
+    currents_recv = mem_reservoir_layer(previous_spikes)
+
+    total_currents = currents_in + currents_recv
+    
+    send_currents_to_spires(total_currents)
+
+    current_spikes_np = read_spikes_from_spires()
+    previous_spikes = torch.from_numpy(current_spikes_np).float().unsqueeze(0)
+
+#pass current spikes np to readout layer