AMD Zynq UltraScale+ ZCU104 (ZU7EV) vs Google Coral Dev Board (Edge TPU)

FPGA / Versal coverage note: 1,243 rows come from the AMD Vitis AI 3.0/3.5 Model Zoo tables; 733 (59%) are image classification. Detection, segmentation and other workloads are thinner, and there is no LLM or speech data for these boards. All are vendor-published (grade D). FPGA / Versal overview.

AMDeval kit (adaptive SoC)No licensed photo yet

Googleedge SBCNo licensed photo yet

Showing the newest results per platform and source. 18 superseded older results are hidden.Showing all results, including 18 superseded by newer results from the same source.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

	AMD Zynq UltraScale+ ZCU104 (ZU7EV)	Google Coral Dev Board (Edge TPU)
Best semantic segmentation	352 FPS D Older data (2023)	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	4 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 AMD Zynq UltraScale+ ZCU104 (ZU7EV) has published object detection results.

Platform	Result	Model · precision · stack	Grade	Power	Source
AMD Zynq UltraScale+ ZCU104 (ZU7EV)	25.43 FPS E2E throughput, multi thread · b=unknown	pt_yolov5s6_coco_1280_1280_17G INT8 · Vitis AI 3.0 (VART) · small (YOLO-s)	D	unknown power n/a no power published	Vitis AI 3.0 Model Zoo performance table Older data (2023)

Google Coral Dev Board (Edge TPU):

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No published object detection results for Google Coral Dev Board (Edge TPU) or AMD Zynq UltraScale+ ZCU104 (ZU7EV) 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 AMD Zynq UltraScale+ ZCU104 (ZU7EV) 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

Only AMD Zynq UltraScale+ ZCU104 (ZU7EV) has published pose estimation results.

Platform	Result	Model · precision · stack	Grade	Power	Source
AMD Zynq UltraScale+ ZCU104 (ZU7EV)	372 FPS E2E throughput, multi thread · b=unknown	pt_movenet_coco_192_192_0.5G INT8 · Vitis AI 3.0 (VART) · mobile-class CNN	D	unknown power n/a no power published	Vitis AI 3.0 Model Zoo performance table Older data (2023)

Google Coral Dev Board (Edge TPU):

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No published pose estimation results for Google Coral Dev Board (Edge TPU) or AMD Zynq UltraScale+ ZCU104 (ZU7EV) 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 AMD Zynq UltraScale+ ZCU104 (ZU7EV) has published image classification results.

Platform	Result	Model · precision · stack	Grade	Power	Source
AMD Zynq UltraScale+ ZCU104 (ZU7EV)	354 FPS E2E throughput, multi thread · b=unknown	pt_OFA-resnet50_imagenet_160_160_0.88_1.8G INT8 · Vitis AI 3.0 (VART) · mid-size CNN	D	unknown power n/a no power published	Vitis AI 3.0 Model Zoo performance table Older data (2023)

Google Coral Dev Board (Edge TPU):

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No published image classification results for Google Coral Dev Board (Edge TPU) or AMD Zynq UltraScale+ ZCU104 (ZU7EV) 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: batch unknown vs 1.

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)
AMD Zynq UltraScale+ ZCU104 (ZU7EV) MIXED: batch unknown vs 1	352 FPS E2E throughput, multi thread · b=unknown	tf2_2d-unet_nuclei_128_128_5.31G INT8 · Vitis AI 3.0 (VART) · mid-size CNN	D	unknown power n/a no power published	Vitis AI 3.0 Model Zoo performance table Older data (2023)

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

speech-to-text

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No published speech-to-text results for AMD Zynq UltraScale+ ZCU104 (ZU7EV) 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 AMD Zynq UltraScale+ ZCU104 (ZU7EV) 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.

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

	AMD Zynq UltraScale+ ZCU104 (ZU7EV)	Google Coral Dev Board (Edge TPU)
Best object detection	463 FPS D Older data (2023)	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	4 of 7	3 of 7

Best published object detection result per platform. n/a = not published; never estimated.

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

object detection

⚠ MIXED COMPARISON — not like-for-like. Baseline mobile-class CNN at batch 1. Differs: model-size class unknown vs mobile-class CNN; batch unknown vs 1.

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)
AMD Zynq UltraScale+ ZCU104 (ZU7EV) MIXED: model-size class unknown vs mobile-class CNN; batch unknown vs 1	463 FPS E2E throughput, multi thread · b=unknown	pt_yolox-nano_coco_416_416_1G INT8 · Vitis AI 3.0 (VART) · unknown	D	unknown power n/a no power published	Vitis AI 3.0 Model Zoo performance table Older data (2023)

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

instance segmentation

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pose estimation

Only AMD Zynq UltraScale+ ZCU104 (ZU7EV) has published pose estimation results.

Platform	Result	Model · precision · stack	Grade	Power	Source
AMD Zynq UltraScale+ ZCU104 (ZU7EV)	372 FPS E2E throughput, multi thread · b=unknown	pt_movenet_coco_192_192_0.5G INT8 · Vitis AI 3.0 (VART) · mobile-class CNN	D	unknown power n/a no power published	Vitis AI 3.0 Model Zoo performance table Older data (2023)

Google Coral Dev Board (Edge TPU):

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image classification

⚠ MIXED COMPARISON — not like-for-like. Baseline mid-size CNN at batch 1. Differs: model-size class mobile-class CNN vs mid-size CNN; batch unknown vs 1.

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)
AMD Zynq UltraScale+ ZCU104 (ZU7EV) MIXED: model-size class mobile-class CNN vs mid-size CNN; batch unknown vs 1	4325 FPS E2E throughput, multi thread · b=unknown	tf_mobilenetv1_0.25_imagenet_128_128_27M INT8 · Vitis AI 3.0 (VART) · mobile-class CNN	D	unknown power n/a no power published	Vitis AI 3.0 Model Zoo performance table Older data (2023)

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

semantic segmentation

⚠ MIXED COMPARISON — not like-for-like. Baseline mid-size CNN at batch 1. Differs: batch unknown vs 1.

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)
AMD Zynq UltraScale+ ZCU104 (ZU7EV) MIXED: batch unknown vs 1	352 FPS E2E throughput, multi thread · b=unknown	tf2_2d-unet_nuclei_128_128_5.31G INT8 · Vitis AI 3.0 (VART) · mid-size CNN	D	unknown power n/a no power published	Vitis AI 3.0 Model Zoo performance table Older data (2023)

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.