AMD Kria KV260 Vision AI Starter Kit (K26 SOM) 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.

AMDSOM starter kit (adaptive SoC)No licensed photo yet

Rockchipedge SoCNo licensed photo yet

Rockchip RK1808 (NPU) is faster in all 2 like-for-like comparisons (largest gap: object detection, 3.05×).

	AMD Kria KV260 Vision AI Starter Kit (K26 SOM)	Rockchip RK1808 (NPU)
Best object detection	276 FPS D Older data (2023)	66.80 FPS C 2026-10-13 (page last edited)
Power	7.50 W spec	n/a
Perf / W	36.76 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 (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 small (YOLO-s), batch 1. Model, precision and stack may still differ.

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 Kria KV260 Vision AI Starter Kit (K26 SOM)	12.20 FPS throughput (DPU only) · b=1	YOLOv8s-Ghost-P2 (Hard-Swish, xView3-SAR) INT8 (Vitis AI quantizer, power-of-two) · Vitis AI 3.5 VART, DPU inference only, 2 threads · small (YOLO-s)	C	7.50 W spec platform spec envelope (not measured with this result)	arXiv 2507.04842v2 (SAR vessel detection on Kria KV260), Table 6 2025-11-17 (published)

✓ Like-for-like: model-size class nano (YOLO-n), batch 1. Model, precision and stack may still differ.

Platform	Result	Model · precision · stack	Grade	Power	Source
Rockchip RK1808 (NPU)	66.80 FPS throughput · b=1	yolov6n 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 Kria KV260 Vision AI Starter Kit (K26 SOM)	33.60 FPS throughput (DPU only) · b=1	YOLOv8n-Ghost (Hard-Swish, xView3-SAR) INT8 (Vitis AI quantizer, power-of-two) · Vitis AI 3.5 VART, DPU inference only, 2 threads · nano (YOLO-n)	C	7.50 W spec platform spec envelope (not measured with this result)	arXiv 2507.04842v2 (SAR vessel detection on Kria KV260), Table 6 2025-11-17 (published)

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 Kria KV260 Vision AI Starter Kit (K26 SOM):

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No published instance segmentation results for AMD Kria KV260 Vision AI Starter Kit (K26 SOM) 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 Kria KV260 Vision AI Starter Kit (K26 SOM) has published pose estimation results.

Platform	Result	Model · precision · stack	Grade	Power	Source
AMD Kria KV260 Vision AI Starter Kit (K26 SOM)	352 FPS E2E throughput, multi thread · b=unknown	pt_movenet_coco_192_192_0.5G INT8 · Vitis AI 3.0 (VART) · mobile-class CNN	D	7.50 W spec reported in result source — spec	Vitis AI 3.0 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 Kria KV260 Vision AI Starter Kit (K26 SOM) 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 Kria KV260 Vision AI Starter Kit (K26 SOM) MIXED: batch unknown vs 1	2432 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	7.50 W spec reported in result source — spec	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 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 Kria KV260 Vision AI Starter Kit (K26 SOM) MIXED: model-size class mid-size CNN vs mobile-class CNN; batch unknown vs 1	183 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	7.50 W spec reported in result source — spec	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 Kria KV260 Vision AI Starter Kit (K26 SOM) 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 Kria KV260 Vision AI Starter Kit (K26 SOM) 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.