Intel N100 vs Terasic DE10-Agilex Development Board (Agilex 7, FPGA AI Suite)
No like-for-like comparison yet: both have published results, but model size or batch differ. Treat the numbers below as indicative only.
| Intel N100 | Terasic DE10-Agilex Development Board (Agilex 7, FPGA AI Suite) | |
|---|---|---|
| Best object detection | 61.02 FPS D 2026-10-01 (published) | 117 FPS D 2026-08-10 (published) |
| Power | n/a | n/a |
| Perf / W | n/a | n/a |
| Price | n/a | n/a |
| Workloads with data | 2 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
| Platform | Result | Model · precision · stack | Grade | Power | Source |
|---|---|---|---|---|---|
| Intel N100 CPU-only | 1.97 FPS throughput (OpenVINO benchmark_app) · b=unknown | ssd-resnet34-1200 INT8 · OpenVINO 2026.4.1 · mid-size CNN | D | unknown power n/a no power published | OpenVINO official performance benchmarks (graph-data-ov.json) 2026-10-01 (published) |
| Terasic DE10-Agilex Development Board (Agilex 7, FPGA AI Suite) MIXED: model-size class unknown vs mid-size CNN; batch 1 vs unknown | 117 FPS throughput · b=1 | yolo-v3-tiny-tf FP16 · FPGA AI Suite 2026.1.1 / OpenVINO; IP throughput (hostless) · unknown | D | unknown power n/a no power published | Altera FPGA AI Suite Handbook (performance tables) 2026-08-10 (published) |
No shared like-for-like group exists for this pair.
instance segmentation
No published instance segmentation results for Intel N100 or Terasic DE10-Agilex Development Board (Agilex 7, FPGA AI Suite) yet.
We only show cited public figures — nothing is estimated to fill gaps. Requests tell us what to source or test next.
pose estimation
No published pose estimation results for Intel N100 or Terasic DE10-Agilex Development Board (Agilex 7, FPGA AI Suite) yet.
We only show cited public figures — nothing is estimated to fill gaps. Requests tell us what to source or test next.
image classification
| Platform | Result | Model · precision · stack | Grade | Power | Source |
|---|---|---|---|---|---|
| Terasic DE10-Agilex Development Board (Agilex 7, FPGA AI Suite) | 1391 FPS throughput · b=1 | mobilenet-v1-1.0-224 not stated in table (set by .arch file) · FPGA AI Suite 2026.1.1 / OpenVINO; IP throughput (hostless) · mobile-class CNN | D | unknown power n/a no power published | Altera FPGA AI Suite Handbook (performance tables) 2026-08-10 (published) |
| Intel N100 CPU-only MIXED: batch unknown vs 1 | 478 FPS throughput (OpenVINO benchmark_app) · b=unknown | mobilenet-v2 INT8 · OpenVINO 2026.4.1 · mobile-class CNN | D | unknown power n/a no power published | OpenVINO official performance benchmarks (graph-data-ov.json) 2026-10-01 (published) |
No shared like-for-like group exists for this pair.
semantic segmentation
Only Terasic DE10-Agilex Development Board (Agilex 7, FPGA AI Suite) has published semantic segmentation results.
| Platform | Result | Model · precision · stack | Grade | Power | Source |
|---|---|---|---|---|---|
| Terasic DE10-Agilex Development Board (Agilex 7, FPGA AI Suite) | 10.26 FPS throughput · b=1 | unet-camvid-onnx-0001 not stated in table (set by .arch file) · FPGA AI Suite 2026.1.1 / OpenVINO; IP throughput (hostless) · mid-size CNN | D | unknown power n/a no power published | Altera FPGA AI Suite Handbook (performance tables) 2026-08-10 (published) |
Intel N100:
No published semantic segmentation results for Intel N100 or Terasic DE10-Agilex Development Board (Agilex 7, FPGA AI Suite) yet.
We only show cited public figures — nothing is estimated to fill gaps. Requests tell us what to source or test next.
speech-to-text
No published speech-to-text results for Intel N100 or Terasic DE10-Agilex Development Board (Agilex 7, FPGA AI Suite) yet.
We only show cited public figures — nothing is estimated to fill gaps. Requests tell us what to source or test next.
LLM generation
No published LLM generation results for Intel N100 or Terasic DE10-Agilex Development Board (Agilex 7, FPGA AI Suite) 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
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.