Coral Dev Board CPU (quad Cortex-A53 @1.5 GHz, CPU only) vs Google Coral Dev Board (Edge TPU)

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Showing the newest results per platform and source. 36 superseded older results are hidden.Showing all results, including 36 superseded by newer results from the same source.

Google Coral Dev Board (Edge TPU) is faster in the only like-for-like comparison (largest gap: semantic segmentation, 33.48×).

	Coral Dev Board CPU (quad Cortex-A53 @1.5 GHz, CPU only)	Google Coral Dev Board (Edge TPU)
Best semantic segmentation	5.24 FPS D Older data (2020)	175 FPS D Older data (2020)
Power	n/a	2.00 W spec
Perf / W	n/a	87.72 FPS/W spec
Price	n/a	n/a
Workloads with data	1 of 7	1 of 7

Best published semantic segmentation 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

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No published object detection results for Coral Dev Board CPU (quad Cortex-A53 @1.5 GHz, CPU only) or Google Coral Dev Board (Edge TPU) yet.
We only show cited public figures — nothing is estimated to fill gaps. Requests tell us what to source or test next.

instance segmentation

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No published instance segmentation results for Coral Dev Board CPU (quad Cortex-A53 @1.5 GHz, CPU only) or Google Coral Dev Board (Edge TPU) yet.
We only show cited public figures — nothing is estimated to fill gaps. Requests tell us what to source or test next.

pose estimation

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No published pose estimation results for Coral Dev Board CPU (quad Cortex-A53 @1.5 GHz, CPU only) or Google Coral Dev Board (Edge TPU) yet.
We only show cited public figures — nothing is estimated to fill gaps. Requests tell us what to source or test next.

image classification

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No published image classification results for Coral Dev Board CPU (quad Cortex-A53 @1.5 GHz, CPU only) or Google Coral Dev Board (Edge TPU) yet.
We only show cited public figures — nothing is estimated to fill gaps. Requests tell us what to source or test next.

semantic segmentation

✓ 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
Google Coral Dev Board (Edge TPU)	175 FPS inference_time · b=1	Unet Mv2 (128x128) INT8 · TensorFlow Lite C++ benchmark (Edge TPU runtime) · mid-size CNN	D	2.00 W spec platform spec envelope (not measured with this result)	Google Coral Edge TPU benchmarks page (Table 1) Older data (2020)
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)

speech-to-text

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No published speech-to-text results for Coral Dev Board CPU (quad Cortex-A53 @1.5 GHz, CPU only) or Google Coral Dev Board (Edge TPU) 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 Coral Dev Board CPU (quad Cortex-A53 @1.5 GHz, CPU only) or Google Coral Dev Board (Edge TPU) yet.
We only show cited public figures — nothing is estimated to fill gaps. Requests tell us what to source or test next.

Google Coral Dev Board (Edge TPU) is faster in all 5 like-for-like comparisons (largest gap: image classification, 116×).

	Coral Dev Board CPU (quad Cortex-A53 @1.5 GHz, CPU only)	Google Coral Dev Board (Edge TPU)
Best object detection	3.55 FPS D Older data (2019)	90.91 FPS D Older data (2019)
Power	n/a	2.00 W spec
Perf / W	n/a	45.45 FPS/W spec
Price	n/a	n/a
Workloads with data	3 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
Google Coral Dev Board (Edge TPU)	90.91 FPS inference_time · b=1	MobileNet v1 SSD (224x224) INT8 · TensorFlow Lite C++ benchmark (Edge TPU runtime) · mobile-class CNN	D	2.00 W spec platform spec envelope (not measured with this result)	Google Coral Edge TPU benchmarks page (Table 1) Older data (2019)
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

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pose estimation

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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
Google Coral Dev Board (Edge TPU)	17.86 FPS inference_time · b=1	ResNet-50 V1 (299x299) INT8 · TensorFlow Lite C++ benchmark (Edge TPU runtime) · mid-size CNN	D	2.00 W spec platform spec envelope (not measured with this result)	Google Coral Edge TPU benchmarks page (Table 1) Older data (2019)
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
Google Coral Dev Board (Edge TPU)	417 FPS inference_time · b=1	MobileNet v1 (224x224) INT8 · TensorFlow Lite C++ benchmark (Edge TPU runtime) · mobile-class CNN	D	2.00 W spec platform spec envelope (not measured with this result)	Google Coral Edge TPU benchmarks page (Table 1) Older data (2019)
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
Google Coral Dev Board (Edge TPU)	500 FPS inference_time · b=1	SqueezeNet (224x224) INT8 · TensorFlow Lite C++ benchmark (Edge TPU runtime) · unknown	D	2.00 W spec platform spec envelope (not measured with this result)	Google Coral Edge TPU benchmarks page (Table 1) Older data (2019)
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

✓ 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
Google Coral Dev Board (Edge TPU)	175 FPS inference_time · b=1	Unet Mv2 (128x128) INT8 · TensorFlow Lite C++ benchmark (Edge TPU runtime) · mid-size CNN	D	2.00 W spec platform spec envelope (not measured with this result)	Google Coral Edge TPU benchmarks page (Table 1) Older data (2020)
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)

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.