Updated elementwise cycle calculation
- Corrected rounding error
- Number of elements depends on ofm format
Signed-off-by: Johan Alfven <johan.alfven@arm.com>
Change-Id: I568d660b7571b6e0ffb131211b3a89c8be4b9295
diff --git a/ethosu/vela/npu_performance.py b/ethosu/vela/npu_performance.py
index 0b8e0a6..450fa7b 100644
--- a/ethosu/vela/npu_performance.py
+++ b/ethosu/vela/npu_performance.py
@@ -35,6 +35,7 @@
from .architecture_features import TensorFormat
from .nn_graph import Graph
from .numeric_util import round_up
+from .numeric_util import round_up_to_int
from .operation import Kernel
from .operation import Op
from .scheduler import Schedule
@@ -388,7 +389,9 @@
# Estimate output cycles
num_ofm_blks = query.ofm_shape.div_round_up(ofm_block).elements()
- cycles_output_blk = _estimate_output_cycles_per_element(arch, op_type, faf_type, query) * ofm_block.elements()
+ cycles_output_blk = round_up_to_int(
+ _estimate_output_cycles_per_element(arch, op_type, faf_type, query) * ofm_block.elements()
+ )
# Scale and bias tensor
if query.const_shape.depth > 0:
@@ -442,8 +445,10 @@
# Elementwise cycle calculation
elif query.npu_block_type == NpuBlockType.ElementWise:
cycles.op_macs = 0
- cycles.op_cycles = int(_estimate_output_cycles_per_element(arch, op_type, faf_type, query)) * int(
- query.ofm_shape.elements()
+ ofm_rounding = Shape4D(list(arch.storage_rounding_quantums[query.ofm_format]))
+ cycles.op_cycles = round_up_to_int(
+ _estimate_output_cycles_per_element(arch, op_type, faf_type, query)
+ * Shape4D.round_up(query.ofm_shape, ofm_rounding).elements()
)
else:
assert False