diff --git a/src/finn/custom_op/fpgadataflow/matrixvectoractivation.py b/src/finn/custom_op/fpgadataflow/matrixvectoractivation.py index 40f625093b62c6f18282066d018a08ed2e587c81..d6285a6f699c774d18998c4c7426ac52362e9dec 100644 --- a/src/finn/custom_op/fpgadataflow/matrixvectoractivation.py +++ b/src/finn/custom_op/fpgadataflow/matrixvectoractivation.py @@ -709,13 +709,6 @@ class MatrixVectorActivation(HLSCustomOp): # ensure all thresholds are integer assert (orig_thres_matrix.astype(np.int32) == orig_thres_matrix).all() ret = orig_thres_matrix - # workaround for vivado_hls threshold bug - if ret[0][0] == 0 and n_thres_steps == 1: - ret = np.copy(ret) - ret[0][0] = 1 - warnings.warn( - "Setting 0-valued first threshold to 1 to avoid vivado_hls bug" - ) # ensure channels = mh , duplicating if necessary if ret.shape[0] == 1: ret = np.tile(ret, (mh, 1)) diff --git a/src/finn/custom_op/fpgadataflow/thresholding_batch.py b/src/finn/custom_op/fpgadataflow/thresholding_batch.py index ce8c31ee9a6cf335dadacff95cf3dbd5cd7590f7..292f70941a3c18a594d904eb4922dc4bce550d82 100644 --- a/src/finn/custom_op/fpgadataflow/thresholding_batch.py +++ b/src/finn/custom_op/fpgadataflow/thresholding_batch.py @@ -319,13 +319,6 @@ class Thresholding_Batch(HLSCustomOp): np.mod(orig_thres_matrix, 1), 0 ).all(), "Need int threshold tensor" ret = orig_thres_matrix - # workaround for vivado_hls threshold bug - if ret[0][0] == 0 and n_thres_steps == 1: - ret = np.copy(ret) - ret[0][0] = 1 - warnings.warn( - "Setting 0-valued first threshold to 1 to avoid vivado_hls bug" - ) # ensure channels = mh , duplicating if necessary if ret.shape[0] == 1: ret = np.tile(ret, (mh, 1)) diff --git a/src/finn/custom_op/fpgadataflow/vectorvectoractivation.py b/src/finn/custom_op/fpgadataflow/vectorvectoractivation.py index 5d996e10d861cab8542fdb7533ef2e3375b04d0b..a2dd3c75dc3c02faab2465e8ac5c70474560bba5 100644 --- a/src/finn/custom_op/fpgadataflow/vectorvectoractivation.py +++ b/src/finn/custom_op/fpgadataflow/vectorvectoractivation.py @@ -418,13 +418,6 @@ class VectorVectorActivation(HLSCustomOp): # ensure all thresholds are integer assert (orig_thres_matrix.astype(np.int32) == orig_thres_matrix).all() ret = orig_thres_matrix - # workaround for vivado_hls threshold bug - if ret[0][0] == 0 and n_thres_steps == 1: - ret = np.copy(ret) - ret[0][0] = 1 - warnings.warn( - "Setting 0-valued first threshold to 1 to avoid vivado_hls bug" - ) # ensure channels = mh , duplicating if necessary if ret.shape[0] == 1: ret = np.tile(ret, (ch, 1))