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| author | Louis Verhaard <louis.verhaard@arm.com> | 2020-09-29 13:57:21 +0200 |
|---|---|---|
| committer | Louis Verhaard <louis.verhaard@arm.com> | 2020-09-30 09:03:22 +0200 |
| commit | 0265f402c7ae1e875470298b4130fcc2f7ab4e23 (patch) | |
| tree | 9cd83f1c755ae118b84c296283bec9152f04f058 | |
| parent | 7ad862ada22dc00c0306ba0f1eafbef98f23c77c (diff) | |
| download | ethos-u-vela-0265f402c7ae1e875470298b4130fcc2f7ab4e23.tar.gz | |
MLBEDSW-3025: Remove SHRAM from reports
SHRAM is removed from performance reports, as the SHRAM numbers only
include LUT usage.
Change-Id: I5d92bb3be9c8e38dad26ac8ef97c84ecb0aff2fa
Signed-off-by: Louis Verhaard <louis.verhaard@arm.com>
| -rw-r--r-- | ethosu/vela/stats_writer.py | 22 |
1 files changed, 14 insertions, 8 deletions
diff --git a/ethosu/vela/stats_writer.py b/ethosu/vela/stats_writer.py index af7b6997..2ea14f2b 100644 --- a/ethosu/vela/stats_writer.py +++ b/ethosu/vela/stats_writer.py @@ -29,9 +29,15 @@ from .tensor import MemArea from .tensor import TensorPurpose +def mem_areas_to_report(): + # Exclude SHRAM, as the SHRAM performance numbers only cover LUT usage + return [area for area in MemArea.all() if area != MemArea.Shram] + + def write_summary_metrics_csv(nng, summary_filename, arch): with open(summary_filename, "w") as f: writer = csv.writer(f) + mem_areas = mem_areas_to_report() labels = [ "experiment", @@ -40,7 +46,7 @@ def write_summary_metrics_csv(nng, summary_filename, arch): labels += ( ["accelerator_configuration", "system_config", "npu_clock", "sram_size"] - + [area.identifier_name() + "_bandwidth" for area in MemArea.all()] + + [area.identifier_name() + "_bandwidth" for area in mem_areas] + ["weights_storage_area", "feature_map_storage_area"] ) @@ -51,10 +57,10 @@ def write_summary_metrics_csv(nng, summary_filename, arch): "passes_before_fusing", "passes_after_fusing", ] - labels += [area.identifier_name() + "_memory_used" for area in MemArea.all()] + labels += [area.identifier_name() + "_memory_used" for area in mem_areas] labels += ["on_chip_flash_bits_per_element", "off_chip_flash_bits_per_element"] - for mem_area in MemArea.all(): + for mem_area in mem_areas: labels += [ mem_area.identifier_name() + "_feature_map_read_bytes", mem_area.identifier_name() + "_feature_map_write_bytes", @@ -77,7 +83,7 @@ def write_summary_metrics_csv(nng, summary_filename, arch): if arch: data_items += ( [arch.accelerator_config, arch.system_config, arch.npu_clock, arch.sram_size / 1024] - + [arch.memory_bandwidths_per_second[mem_area] / 1000.0 / 1000 / 1000 for mem_area in MemArea.all()] + + [arch.memory_bandwidths_per_second[mem_area] / 1000.0 / 1000 / 1000 for mem_area in mem_areas] + [ arch.tensor_storage_mem_area[TensorPurpose.Weights].display_name(), arch.tensor_storage_mem_area[TensorPurpose.FeatureMap].display_name(), @@ -94,14 +100,14 @@ def write_summary_metrics_csv(nng, summary_filename, arch): n_cascaded_passes = sum(len(sg.cascaded_passes) for sg in nng.subgraphs) data_items += [midpoint_fps, nng.batch_size, midpoint_inference_time, n_passes, n_cascaded_passes] - data_items += [nng.memory_used.get(mem_area, 0) / 1024.0 for mem_area in MemArea.all()] + data_items += [nng.memory_used.get(mem_area, 0) / 1024.0 for mem_area in mem_areas] data_items += [ nng.bits_per_element.get(MemArea.OnChipFlash, 0.0), nng.bits_per_element.get(MemArea.OffChipFlash, 0.0), ] - for mem_area in MemArea.all(): + for mem_area in mem_areas: bws = nng.bandwidths[mem_area] total_bw = np.sum(bws) weight_bws = bws[TensorPurpose.Weights] @@ -144,7 +150,7 @@ def write_pass_metrics_csv(nng, pass_filename): ) bandwidth_names = [] bandwidth_indices = [] - for mem_area in MemArea.all(): + for mem_area in mem_areas_to_report(): for purpose, purpose_candidates in purpose_list: for direction, direction_candidates in direction_list: label = "bytes_%s_%s_%s" % (mem_area.identifier_name(), purpose, direction) @@ -231,7 +237,7 @@ def print_performance_metrics_for_strat( f=sys.stdout, ): - orig_mem_areas_labels = [(v, v.display_name()) for v in MemArea.all()] + orig_mem_areas_labels = [(v, v.display_name()) for v in mem_areas_to_report()] midpoint_inference_time = cycles[PassCycles.Total] / arch.npu_clock if midpoint_inference_time > 0: |