Index

_

__getitem__() (pyrtlnet.wire_matrix_2d.WireMatrix2D method) __init__() (pyrtlnet.cli_util.Accuracy method) (pyrtlnet.cli_util.PrintElapsedTime method) (pyrtlnet.numpy_inference.NumPyInference method) (pyrtlnet.pyrtl_inference.PyRTLInference method) (pyrtlnet.saved_tensors.QuantizedLayer method) (pyrtlnet.saved_tensors.SavedTensors method) (pyrtlnet.wire_matrix_2d.WireMatrix2D method)

A

Accuracy (class in pyrtlnet.cli_util)

add_common_arguments() (in module pyrtlnet.inference_util)

B

batched_images() (in module pyrtlnet.inference_util)

bias (pyrtlnet.saved_tensors.QuantizedLayer attribute) bitwidth (pyrtlnet.wire_matrix_2d.WireMatrix2D attribute)

D

display() (pyrtlnet.cli_util.Accuracy method)

display_image() (in module pyrtlnet.cli_util) display_outputs() (in module pyrtlnet.cli_util)

E

evaluate_model() (in module pyrtlnet.tensorflow_training)

G

get_tensor_scale_zero() (in module pyrtlnet.training_util)

I

input_scale (pyrtlnet.saved_tensors.SavedTensors attribute)

input_zero (pyrtlnet.saved_tensors.SavedTensors attribute) inspect() (pyrtlnet.wire_matrix_2d.WireMatrix2D method)

L

layer (pyrtlnet.saved_tensors.SavedTensors attribute) load_mnist_data() (in module pyrtlnet.inference_util)

load_mnist_images() (in module pyrtlnet.mnist_util) load_tflite_model() (in module pyrtlnet.litert_inference)

M

m0 (pyrtlnet.saved_tensors.QuantizedLayer attribute) make_argmax() (in module pyrtlnet.pyrtl_matrix) make_axi_lite_subordinate() (in module pyrtlnet.pyrtl_axi) make_axi_stream_subordinate() (in module pyrtlnet.pyrtl_axi) make_concatenated_value() (in module pyrtlnet.wire_matrix_2d) make_elementwise_add() (in module pyrtlnet.pyrtl_matrix) make_elementwise_normalize() (in module pyrtlnet.pyrtl_matrix) make_elementwise_relu() (in module pyrtlnet.pyrtl_matrix) make_input_memblock_data() (in module pyrtlnet.pyrtl_matrix) make_outputs() (pyrtlnet.wire_matrix_2d.WireMatrix2D method) make_provided_inputs() (pyrtlnet.wire_matrix_2d.WireMatrix2D method) make_systolic_array() (in module pyrtlnet.pyrtl_matrix) minimum_bitwidth() (in module pyrtlnet.pyrtl_matrix)

module pyrtlnet.cli_util pyrtlnet.inference_util pyrtlnet.litert_inference pyrtlnet.mnist_util pyrtlnet.numpy_inference pyrtlnet.pyrtl_axi pyrtlnet.pyrtl_inference pyrtlnet.pyrtl_matrix pyrtlnet.saved_tensors pyrtlnet.tensorflow_training pyrtlnet.training_util pyrtlnet.wire_matrix_2d

N

n (pyrtlnet.saved_tensors.QuantizedLayer attribute) normalization_constants() (in module pyrtlnet.saved_tensors)

normalize() (in module pyrtlnet.numpy_inference) num_systolic_array_cycles() (in module pyrtlnet.pyrtl_matrix) NumPyInference (class in pyrtlnet.numpy_inference)

P

preprocess_image() (in module pyrtlnet.inference_util) PrintElapsedTime (class in pyrtlnet.cli_util) PyRTLInference (class in pyrtlnet.pyrtl_inference) pyrtlnet.cli_util module pyrtlnet.inference_util module pyrtlnet.litert_inference module pyrtlnet.mnist_util module pyrtlnet.numpy_inference module

pyrtlnet.pyrtl_axi module pyrtlnet.pyrtl_inference module pyrtlnet.pyrtl_matrix module pyrtlnet.saved_tensors module pyrtlnet.tensorflow_training module pyrtlnet.training_util module pyrtlnet.wire_matrix_2d module

Q

quantize_model() (in module pyrtlnet.tensorflow_training)

quantized_matmul() (in module pyrtlnet.numpy_inference) QuantizedLayer (class in pyrtlnet.saved_tensors)

R

ready (pyrtlnet.wire_matrix_2d.WireMatrix2D attribute) relu() (in module pyrtlnet.numpy_inference)

run() (pyrtlnet.numpy_inference.NumPyInference method) run_tflite_model() (in module pyrtlnet.litert_inference)

S

saturating_truncate() (in module pyrtlnet.pyrtl_matrix) save_mnist_data() (in module pyrtlnet.training_util) save_tensors() (in module pyrtlnet.training_util) SavedTensors (class in pyrtlnet.saved_tensors)

scale (pyrtlnet.saved_tensors.QuantizedLayer attribute) shape (pyrtlnet.wire_matrix_2d.WireMatrix2D attribute) simulate() (pyrtlnet.pyrtl_inference.PyRTLInference method) simulate_axi_lite_read() (in module pyrtlnet.pyrtl_axi) simulate_axi_stream_send() (in module pyrtlnet.pyrtl_axi)

T

train_unquantized_model() (in module pyrtlnet.tensorflow_training)

transpose() (pyrtlnet.wire_matrix_2d.WireMatrix2D method)

U

update() (pyrtlnet.cli_util.Accuracy method)

V

valid (pyrtlnet.wire_matrix_2d.WireMatrix2D attribute)

W

weight (pyrtlnet.saved_tensors.QuantizedLayer attribute)

WireMatrix2D (class in pyrtlnet.wire_matrix_2d)

Z

zero (pyrtlnet.saved_tensors.QuantizedLayer attribute)