diff --git a/ModNEF_Sources/modules/neurons/ShiftLif/shiftlif_parallel.vhd b/ModNEF_Sources/modules/neurons/ShiftLif/shiftlif_parallel.vhd
index 6c6989f1f7094fcc5701801c400b3fdb7c2f5e72..32a3aca72754c8862b01753e55ee7938935498a4 100644
--- a/ModNEF_Sources/modules/neurons/ShiftLif/shiftlif_parallel.vhd
+++ b/ModNEF_Sources/modules/neurons/ShiftLif/shiftlif_parallel.vhd
@@ -127,7 +127,7 @@ architecture Behavioral of ShiftLif_Parallel is
end component;
-- type definition
- type transmission_state_t is (idle, voltage_update, check_arbitration, request, accept, wait_arbitration);
+ type transmission_state_t is (idle, voltage_update, check_arbitration, request, accept, wait_arbitration, arbitration_finish);
type reception_state_t is (idle, request, get_data);
-- ram signals
@@ -249,9 +249,8 @@ begin
when check_arbitration =>
if spikes = no_spike then
- transmission_state <= idle;
+ transmission_state <= arbitration_finish;
o_emu_busy <= '0';
- tr_fsm_en := '0';
else
transmission_state <= request;
arb_spikes <= spikes;
@@ -278,12 +277,15 @@ begin
when wait_arbitration =>
start_arb <= '0';
if arb_busy = '0' then
- transmission_state <= idle;
- o_emu_busy <= '0';
- tr_fsm_en := '0';
+ transmission_state <= arbitration_finish;
else
transmission_state <= wait_arbitration;
end if;
+
+ when arbitration_finish =>
+ transmission_state <= idle;
+ o_emu_busy <= '0';
+ tr_fsm_en := '0';
end case;
end if;
end if;
@@ -337,3 +339,4 @@ begin
end generate neuron_generation;
end Behavioral;
+
diff --git a/modneflib/modnef/modnef_driver/drivers/xstep_driver.py b/modneflib/modnef/modnef_driver/drivers/xstep_driver.py
index 778b95b1a72a37f0cd6a6a61a0e57aa3c37baad9..6b5b21603d31eef495e290dbe03402801cf5df6c 100644
--- a/modneflib/modnef/modnef_driver/drivers/xstep_driver.py
+++ b/modneflib/modnef/modnef_driver/drivers/xstep_driver.py
@@ -160,8 +160,9 @@ class XStep_Driver(ModNEF_Driver):
step_send += 1
if step == len(sample_aer)-1:
+ print(len(data))
emulation_result = self.rust_driver.data_transmission(data, reset_membrane)
-
+ print("hi")
res_step = self._unpack_data(emulation_result)
for rs in res_step:
for aer in rs:
diff --git a/modneflib/modnef/templates/dataset.py b/modneflib/modnef/templates/dataset.py
new file mode 100644
index 0000000000000000000000000000000000000000..e0b86fc5a43069f74cf733e33b7dd16c7d8360af
--- /dev/null
+++ b/modneflib/modnef/templates/dataset.py
@@ -0,0 +1,20 @@
+import os
+import tonic
+from torch.utils.data import DataLoader
+
+"""DataSet Definition"""
+dataset_path = f"{os.environ['HOME']}/datasets"
+
+# data set definition, change to your dataset
+sensor_size = tonic.datasets.NMNIST.sensor_size
+frame_transform = tonic.transforms.ToFrame(sensor_size=sensor_size, n_time_bins=5)
+
+train_set = tonic.datasets.NMNIST(save_to=dataset_path, train=True, transform=frame_transform)
+test_set = tonic.datasets.NMNIST(save_to=dataset_path, train=False, transform=frame_transform)
+
+# batch loader
+batch_size = 64
+
+trainLoader = DataLoader(train_set, batch_size=batch_size, shuffle=True, drop_last = True, collate_fn = tonic.collation.PadTensors(batch_first=True))
+testLoader = DataLoader(test_set, batch_size=batch_size, shuffle=True, drop_last = True, collate_fn = tonic.collation.PadTensors(batch_first=True))
+validationLoader = None
\ No newline at end of file
diff --git a/modneflib/modnef/templates/evaluation.py b/modneflib/modnef/templates/evaluation.py
index ebcc8f8f457513ab00649a1ccae38446d243a2cf..91cba66dd98812a08f9cdbe433d2c6db85887f6e 100644
--- a/modneflib/modnef/templates/evaluation.py
+++ b/modneflib/modnef/templates/evaluation.py
@@ -7,6 +7,7 @@ import torch
from run_lib import *
import sys
from model import MyModel
+from dataset import *
if __name__ == "__main__":
@@ -53,20 +54,7 @@ if __name__ == "__main__":
conf_matrix_file = "confusion_matrix.png"
conf_matrix_classes = [str(i) for i in range(10)]
- """DataSet Definition"""
- dataset_path = f"{os.environ['HOME']}/datasets"
-
- # data set definition, change to your dataset
- sensor_size = tonic.datasets.NMNIST.sensor_size
- frame_transform = tonic.transforms.ToFrame(sensor_size=sensor_size, n_time_bins=10)
-
- test_set = tonic.datasets.NMNIST(save_to=dataset_path, train=False, transform=frame_transform)
-
- # batch loader
- batch_size = 64
- testLoader = DataLoader(test_set, batch_size=batch_size, shuffle=True, drop_last = True, collate_fn = tonic.collation.PadTensors(batch_first=True))
-
if kind == "eval":
acc, y_pred, y_true = evaluation(
model=model,
@@ -99,7 +87,7 @@ if __name__ == "__main__":
exit(-1)
if save_conf_matrix:
- confusion_matrix(
+ conf_matrix(
y_true=y_true,
y_pred=y_pred,
file_name=conf_matrix_file,
diff --git a/modneflib/modnef/templates/train.py b/modneflib/modnef/templates/train.py
index b67de4512337ec34d5c96066df3ed7e8f815a98a..0f481251196a869236f019f777c3ce83f9e01f5e 100644
--- a/modneflib/modnef/templates/train.py
+++ b/modneflib/modnef/templates/train.py
@@ -6,6 +6,7 @@ from snntorch.surrogate import fast_sigmoid
from run_lib import *
import torch
from model import MyModel
+from dataset import *
if __name__ == "__main__":
@@ -31,23 +32,6 @@ if __name__ == "__main__":
save_plot = False
save_history = False
output_path = "."
-
- """DataSet Definition"""
- dataset_path = f"{os.environ['HOME']}/datasets"
-
- # data set definition, change to your dataset
- sensor_size = tonic.datasets.NMNIST.sensor_size
- frame_transform = tonic.transforms.ToFrame(sensor_size=sensor_size, n_time_bins=5)
-
- train_set = tonic.datasets.NMNIST(save_to=dataset_path, train=True, transform=frame_transform)
- test_set = tonic.datasets.NMNIST(save_to=dataset_path, train=False, transform=frame_transform)
-
- # batch loader
- batch_size = 64
-
- trainLoader = DataLoader(train_set, batch_size=batch_size, shuffle=True, drop_last = True, collate_fn = tonic.collation.PadTensors(batch_first=True))
- testLoader = DataLoader(test_set, batch_size=batch_size, shuffle=True, drop_last = True, collate_fn = tonic.collation.PadTensors(batch_first=True))
- validationLoader = None
train(
model=model,
diff --git a/modneflib/modnef/templates/vhdl_generation.py b/modneflib/modnef/templates/vhdl_generation.py
index fcca214d8959aa06374ae7c4c93aa428ab9fd7a2..563d1a1fb7de009c76d06f4efdd45217cd0c65f7 100644
--- a/modneflib/modnef/templates/vhdl_generation.py
+++ b/modneflib/modnef/templates/vhdl_generation.py
@@ -6,6 +6,7 @@ from snntorch.surrogate import fast_sigmoid
from run_lib import *
import torch
from model import MyModel
+from dataset import *
if __name__ == "__main__":
@@ -33,21 +34,7 @@ if __name__ == "__main__":
"""VHDL file definition"""
output_path = "."
file_name = "template_vhdl_model.vhd"
- driver_config_path = "driver_config"
-
- """DataSet Definition"""
- dataset_path = f"{os.environ['HOME']}/datasets"
-
- # data set definition, change to your dataset
- sensor_size = tonic.datasets.NMNIST.sensor_size
- frame_transform = tonic.transforms.ToFrame(sensor_size=sensor_size, n_time_bins=10)
-
- test_set = tonic.datasets.NMNIST(save_to=dataset_path, train=False, transform=frame_transform)
-
- # batch loader
- batch_size = 64
-
- testLoader = DataLoader(test_set, batch_size=batch_size, shuffle=True, drop_last = True, collate_fn = tonic.collation.PadTensors(batch_first=True))
+ driver_config_path = "driver_config.yml"
acc, y_pred, y_true = hardware_estimation(
model=model,