AMD Alveo V70 (Versal AI Core, XDNA AIE-ML) vs Rockchip RK1808 (NPU)

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.

AMDPCIe accelerator cardNo licensed photo yet

Rockchipedge SoCNo licensed photo yet

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

	AMD Alveo V70 (Versal AI Core, XDNA AIE-ML)	Rockchip RK1808 (NPU)
Best object detection	6100 FPS D Older data (2023)	66.80 FPS C 2026-10-13 (page last edited)
Power	75.00 W spec	n/a
Perf / W	81.34 FPS/W spec	n/a
Price	n/a	n/a
Workloads with data	4 of 7	4 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 small (YOLO-s) at batch 1. Differs: model-size class mobile-class CNN vs small (YOLO-s); batch unknown vs 1.

Platform	Result	Model · precision · stack	Grade	Power	Source
Rockchip RK1808 (NPU)	37.20 FPS throughput · b=1	yolov5s_relu INT8 · RKNN-Toolkit2 / RKNPU (rknn_model_zoo) · small (YOLO-s)	C	unknown power n/a no power published	Rockchip rknn_model_zoo README benchmark table 2026-10-13 (page last edited)
AMD Alveo V70 (Versal AI Core, XDNA AIE-ML) MIXED: model-size class mobile-class CNN vs small (YOLO-s); batch unknown vs 1	6100 FPS E2E throughput, multi thread · b=unknown	tf_ssdmobilenetv1_3.5 INT8 · Vitis AI 3.5 (VART) · mobile-class CNN	D	75.00 W spec reported in result source — spec	Vitis AI 3.5 Model Zoo performance table Older data (2023)

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

instance segmentation

Only Rockchip RK1808 (NPU) has published instance segmentation results.

Platform	Result	Model · precision · stack	Grade	Power	Source
Rockchip RK1808 (NPU)	49.60 FPS throughput · b=1	yolov5n-seg INT8 · RKNN-Toolkit2 / RKNPU (rknn_model_zoo) · nano (YOLO-n)	C	unknown power n/a no power published	Rockchip rknn_model_zoo README benchmark table 2026-10-13 (page last edited)

AMD Alveo V70 (Versal AI Core, XDNA AIE-ML):

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No published instance segmentation results for AMD Alveo V70 (Versal AI Core, XDNA AIE-ML) or Rockchip RK1808 (NPU) 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 Alveo V70 (Versal AI Core, XDNA AIE-ML) has published pose estimation results.

Platform	Result	Model · precision · stack	Grade	Power	Source
AMD Alveo V70 (Versal AI Core, XDNA AIE-ML)	8327 FPS E2E throughput, multi thread · b=unknown	pt_movenet_3.5 INT8 · Vitis AI 3.5 (VART) · mobile-class CNN	D	75.00 W spec reported in result source — spec	Vitis AI 3.5 Model Zoo performance table Older data (2023)

Rockchip RK1808 (NPU):

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No published pose estimation results for Rockchip RK1808 (NPU) or AMD Alveo V70 (Versal AI Core, XDNA AIE-ML) yet.
We only show cited public figures — nothing is estimated to fill gaps. Requests tell us what to source or test next.

image classification

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

Platform	Result	Model · precision · stack	Grade	Power	Source
Rockchip RK1808 (NPU)	170 FPS throughput · b=1	mobilenetv2-12 INT8 · RKNN-Toolkit2 / RKNPU (rknn_model_zoo) · mobile-class CNN	C	unknown power n/a no power published	Rockchip rknn_model_zoo README benchmark table 2026-10-13 (page last edited)
AMD Alveo V70 (Versal AI Core, XDNA AIE-ML) MIXED: batch unknown vs 1	63109 FPS E2E throughput, multi thread · b=unknown	tf_mobilenetv1-0.25_3.5 INT8 · Vitis AI 3.5 (VART) · mobile-class CNN	D	75.00 W spec reported in result source — spec	Vitis AI 3.5 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 mobile-class CNN at batch 1. Differs: model-size class mid-size CNN vs mobile-class CNN; batch unknown vs 1.

Platform	Result	Model · precision · stack	Grade	Power	Source
Rockchip RK1808 (NPU)	4.40 FPS throughput · b=1	deeplab-v3-plus-mobilenet-v2 INT8 · RKNN-Toolkit2 / RKNPU (rknn_model_zoo) · mobile-class CNN	C	unknown power n/a no power published	Rockchip rknn_model_zoo README benchmark table 2026-10-13 (page last edited)
AMD Alveo V70 (Versal AI Core, XDNA AIE-ML) MIXED: model-size class mid-size CNN vs mobile-class CNN; batch unknown vs 1	1177 FPS E2E throughput, multi thread · b=unknown	tf2_2D-UNET_3.5 INT8 · Vitis AI 3.5 (VART) · mid-size CNN	D	75.00 W spec reported in result source — spec	Vitis AI 3.5 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 Alveo V70 (Versal AI Core, XDNA AIE-ML) or Rockchip RK1808 (NPU) 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 Alveo V70 (Versal AI Core, XDNA AIE-ML) or Rockchip RK1808 (NPU) yet.
We only show cited public figures — nothing is estimated to fill gaps. Requests tell us what to source or test next.

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.