Axelera Metis Embedded 113m M.2 card vs Coral Dev Board CPU (quad Cortex-A53 @1.5 GHz, CPU only)

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Showing the newest results per platform and source. 18 superseded older results are hidden.Showing all results, including 18 superseded by newer results from the same source.

No like-for-like comparison yet: both have published results, but model size or batch differ. Treat the numbers below as indicative only.

	Axelera Metis Embedded 113m M.2 card	Coral Dev Board CPU (quad Cortex-A53 @1.5 GHz, CPU only)
Best semantic segmentation	294 FPS C 2026-10-15 (page last edited)	5.24 FPS D Older data (2020)
Power	n/a	n/a
Perf / W	n/a	n/a
Price	n/a	n/a
Workloads with data	5 of 7	1 of 7

Best published semantic segmentation result per platform. n/a = not published; never estimated.

Show data — all workloads, sources and like-for-like groups

object detection

Only Axelera Metis Embedded 113m M.2 card has published object detection results.

Platform	Result	Model · precision · stack	Grade	Power	Source
Axelera Metis Embedded 113m M.2 card	91.00 FPS throughput · b=1	RetinaFace - Resnet50 INT8 · Voyager SDK (GStreamer pipeline) · mid-size CNN	C	unknown power n/a no power published	Axelera Voyager SDK model zoo (docs/reference/models/model-zoo.md) 2026-10-15 (page last edited)

Coral Dev Board CPU (quad Cortex-A53 @1.5 GHz, CPU only):

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No published object detection results for Coral Dev Board CPU (quad Cortex-A53 @1.5 GHz, CPU only) or Axelera Metis Embedded 113m M.2 card yet.
We only show cited public figures — nothing is estimated to fill gaps. Requests tell us what to source or test next.

instance segmentation

Only Axelera Metis Embedded 113m M.2 card has published instance segmentation results.

Platform	Result	Model · precision · stack	Grade	Power	Source
Axelera Metis Embedded 113m M.2 card	186 FPS throughput · b=1	YOLACT-ResNet50 INT8 · Voyager SDK (GStreamer pipeline) · mid-size CNN	C	unknown power n/a no power published	Axelera Voyager SDK model zoo (docs/reference/models/model-zoo.md) 2026-10-15 (page last edited)

Coral Dev Board CPU (quad Cortex-A53 @1.5 GHz, CPU only):

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No published instance segmentation results for Coral Dev Board CPU (quad Cortex-A53 @1.5 GHz, CPU only) or Axelera Metis Embedded 113m M.2 card yet.
We only show cited public figures — nothing is estimated to fill gaps. Requests tell us what to source or test next.

pose estimation

Only Axelera Metis Embedded 113m M.2 card has published pose estimation results.

Platform	Result	Model · precision · stack	Grade	Power	Source
Axelera Metis Embedded 113m M.2 card	746 FPS throughput · b=1	YOLOv8n-pose INT8 · Voyager SDK (GStreamer pipeline) · nano (YOLO-n)	C	unknown power n/a no power published	Axelera Voyager SDK model zoo (docs/reference/models/model-zoo.md) 2026-10-15 (page last edited)

Coral Dev Board CPU (quad Cortex-A53 @1.5 GHz, CPU only):

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No published pose estimation results for Coral Dev Board CPU (quad Cortex-A53 @1.5 GHz, CPU only) or Axelera Metis Embedded 113m M.2 card yet.
We only show cited public figures — nothing is estimated to fill gaps. Requests tell us what to source or test next.

image classification

Only Axelera Metis Embedded 113m M.2 card has published image classification results.

Platform	Result	Model · precision · stack	Grade	Power	Source
Axelera Metis Embedded 113m M.2 card	4257 FPS throughput · b=1	MobileNetV4-small INT8 · Voyager SDK (GStreamer pipeline) · mobile-class CNN	C	unknown power n/a no power published	Axelera Voyager SDK model zoo (docs/reference/models/model-zoo.md) 2026-10-15 (page last edited)

Coral Dev Board CPU (quad Cortex-A53 @1.5 GHz, CPU only):

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No published image classification results for Coral Dev Board CPU (quad Cortex-A53 @1.5 GHz, CPU only) or Axelera Metis Embedded 113m M.2 card yet.
We only show cited public figures — nothing is estimated to fill gaps. Requests tell us what to source or test next.

semantic segmentation

⚠ MIXED COMPARISON — not like-for-like. Baseline mid-size CNN at batch 1. Differs: model-size class nano (YOLO-n) vs mid-size CNN.

Platform	Result	Model · precision · stack	Grade	Power	Source
Coral Dev Board CPU (quad Cortex-A53 @1.5 GHz, CPU only) CPU-only	5.24 FPS inference_time · b=1	Unet Mv2 (128x128) TFLite on CPU · TensorFlow Lite C++ benchmark (Edge TPU runtime) · mid-size CNN	D	unknown power n/a no power published	Google Coral Edge TPU benchmarks page (Table 1) Older data (2020)
Axelera Metis Embedded 113m M.2 card MIXED: model-size class nano (YOLO-n) vs mid-size CNN	294 FPS throughput · b=1	YOLO26n-sem INT8 · Voyager SDK (GStreamer pipeline) · nano (YOLO-n)	C	unknown power n/a no power published	Axelera Voyager SDK model zoo (docs/reference/models/model-zoo.md) 2026-10-15 (page last edited)

No shared like-for-like group exists for this pair.

speech-to-text

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No published speech-to-text results for Axelera Metis Embedded 113m M.2 card or Coral Dev Board CPU (quad Cortex-A53 @1.5 GHz, CPU only) yet.
We only show cited public figures — nothing is estimated to fill gaps. Requests tell us what to source or test next.

LLM generation

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No published LLM generation results for Axelera Metis Embedded 113m M.2 card or Coral Dev Board CPU (quad Cortex-A53 @1.5 GHz, CPU only) yet.
We only show cited public figures — nothing is estimated to fill gaps. Requests tell us what to source or test next.

Axelera Metis Embedded 113m M.2 card is faster in all 4 like-for-like comparisons (largest gap: image classification, 7740×).

	Axelera Metis Embedded 113m M.2 card	Coral Dev Board CPU (quad Cortex-A53 @1.5 GHz, CPU only)
Best object detection	3004 FPS C 2026-10-15 (page last edited)	3.55 FPS D Older data (2019)
Power	n/a	n/a
Perf / W	n/a	n/a
Price	n/a	n/a
Workloads with data	5 of 7	3 of 7

Best published object detection result per platform (may be from different like-for-like groups — see data). n/a = not published; never estimated.

Show data — all workloads, sources and like-for-like groups

object detection

✓ Like-for-like: model-size class mobile-class CNN, batch 1. Model, precision and stack may still differ.

Platform	Result	Model · precision · stack	Grade	Power	Source
Axelera Metis Embedded 113m M.2 card	3004 FPS throughput · b=1	SSD-MobileNetV1 INT8 · Voyager SDK (GStreamer pipeline) · mobile-class CNN	C	unknown power n/a no power published	Axelera Voyager SDK model zoo (docs/reference/models/model-zoo.md) 2026-10-15 (page last edited)
Coral Dev Board CPU (quad Cortex-A53 @1.5 GHz, CPU only) CPU-only	3.55 FPS inference_time · b=1	MobileNet v2 SSD (224x224) TFLite on CPU · TensorFlow Lite C++ benchmark (Edge TPU runtime) · mobile-class CNN	D	unknown power n/a no power published	Google Coral Edge TPU benchmarks page (Table 1) Older data (2019)

instance segmentation

Only Axelera Metis Embedded 113m M.2 card has published instance segmentation results.

Platform	Result	Model · precision · stack	Grade	Power	Source
Axelera Metis Embedded 113m M.2 card	186 FPS throughput · b=1	YOLACT-ResNet50 INT8 · Voyager SDK (GStreamer pipeline) · mid-size CNN	C	unknown power n/a no power published	Axelera Voyager SDK model zoo (docs/reference/models/model-zoo.md) 2026-10-15 (page last edited)

Coral Dev Board CPU (quad Cortex-A53 @1.5 GHz, CPU only):

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pose estimation

Only Axelera Metis Embedded 113m M.2 card has published pose estimation results.

Platform	Result	Model · precision · stack	Grade	Power	Source
Axelera Metis Embedded 113m M.2 card	746 FPS throughput · b=1	YOLOv8n-pose INT8 · Voyager SDK (GStreamer pipeline) · nano (YOLO-n)	C	unknown power n/a no power published	Axelera Voyager SDK model zoo (docs/reference/models/model-zoo.md) 2026-10-15 (page last edited)

Coral Dev Board CPU (quad Cortex-A53 @1.5 GHz, CPU only):

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image classification

✓ Like-for-like: model-size class mid-size CNN, batch 1. Model, precision and stack may still differ.

Platform	Result	Model · precision · stack	Grade	Power	Source
Axelera Metis Embedded 113m M.2 card	4412 FPS throughput · b=1	ResNet-10t INT8 · Voyager SDK (GStreamer pipeline) · mid-size CNN	C	unknown power n/a no power published	Axelera Voyager SDK model zoo (docs/reference/models/model-zoo.md) 2026-10-15 (page last edited)
Coral Dev Board CPU (quad Cortex-A53 @1.5 GHz, CPU only) CPU-only	0.570 FPS inference_time · b=1	ResNet-50 V1 (299x299) TFLite on CPU · TensorFlow Lite C++ benchmark (Edge TPU runtime) · mid-size CNN	D	unknown power n/a no power published	Google Coral Edge TPU benchmarks page (Table 1) Older data (2019)

✓ Like-for-like: model-size class mobile-class CNN, batch 1. Model, precision and stack may still differ.

Platform	Result	Model · precision · stack	Grade	Power	Source
Axelera Metis Embedded 113m M.2 card	4257 FPS throughput · b=1	MobileNetV4-small INT8 · Voyager SDK (GStreamer pipeline) · mobile-class CNN	C	unknown power n/a no power published	Axelera Voyager SDK model zoo (docs/reference/models/model-zoo.md) 2026-10-15 (page last edited)
Coral Dev Board CPU (quad Cortex-A53 @1.5 GHz, CPU only) CPU-only	8.20 FPS inference_time · b=1	MobileNet v2 (224x224) TFLite on CPU · TensorFlow Lite C++ benchmark (Edge TPU runtime) · mobile-class CNN	D	unknown power n/a no power published	Google Coral Edge TPU benchmarks page (Table 1) Older data (2019)

✓ Like-for-like: model-size class unknown, batch 1 (size or batch not stated by source — unverified). Model, precision and stack may still differ.

Platform	Result	Model · precision · stack	Grade	Power	Source
Axelera Metis Embedded 113m M.2 card	5881 FPS throughput · b=1	SqueezeNet 1.1 INT8 · Voyager SDK (GStreamer pipeline) · unknown	C	unknown power n/a no power published	Axelera Voyager SDK model zoo (docs/reference/models/model-zoo.md) 2026-10-15 (page last edited)
Coral Dev Board CPU (quad Cortex-A53 @1.5 GHz, CPU only) CPU-only	4.31 FPS inference_time · b=1	SqueezeNet (224x224) TFLite on CPU · TensorFlow Lite C++ benchmark (Edge TPU runtime) · unknown	D	unknown power n/a no power published	Google Coral Edge TPU benchmarks page (Table 1) Older data (2019)

semantic segmentation

⚠ MIXED COMPARISON — not like-for-like. Baseline mid-size CNN at batch 1. Differs: model-size class nano (YOLO-n) vs mid-size CNN.

Platform	Result	Model · precision · stack	Grade	Power	Source
Coral Dev Board CPU (quad Cortex-A53 @1.5 GHz, CPU only) CPU-only	5.24 FPS inference_time · b=1	Unet Mv2 (128x128) TFLite on CPU · TensorFlow Lite C++ benchmark (Edge TPU runtime) · mid-size CNN	D	unknown power n/a no power published	Google Coral Edge TPU benchmarks page (Table 1) Older data (2020)
Axelera Metis Embedded 113m M.2 card MIXED: model-size class nano (YOLO-n) vs mid-size CNN	294 FPS throughput · b=1	YOLO26n-sem INT8 · Voyager SDK (GStreamer pipeline) · nano (YOLO-n)	C	unknown power n/a no power published	Axelera Voyager SDK model zoo (docs/reference/models/model-zoo.md) 2026-10-15 (page last edited)

No shared like-for-like group exists for this pair.

speech-to-text

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LLM generation

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How to read these numbers

Every number is labelled. Confidence: A MLPerf closed, audited · B documented third-party method · C community / review · D vendor-published, not independently verified · E estimate. Power basis: measured with the result · spec vendor spec/envelope · power n/a not published.

Machine-readable: /find.json (see llms.txt); full tables are in “Show data”. Confidence grades: A audited MLPerf closed · B documented third-party · C community · D vendor-published · E estimate. Unknown values are shown as unknown, never estimated. Generated 2026-10-16 from public sources.