Google Coral USB Accelerator (Edge TPU) on Xeon Gold 6154 host, USB 3.0 vs Terasic DE10-Agilex Development Board (Agilex 7, FPGA AI Suite)
Google Coral USB Accelerator (Edge TPU) on Xeon Gold 6154 host, USB 3.0 is faster in the only like-for-like comparison (largest gap: semantic segmentation, 29.54×).
| Google Coral USB Accelerator (Edge TPU) on Xeon Gold 6154 host, USB 3.0 | Terasic DE10-Agilex Development Board (Agilex 7, FPGA AI Suite) | |
|---|---|---|
| Best semantic segmentation | 303 FPS D Older data (2020) | 10.26 FPS D 2026-08-10 (published) |
| Power | 2.00 W spec | n/a |
| Perf / W | 152 FPS/W spec | n/a |
| Price | n/a | n/a |
| Workloads with data | 1 of 7 | 3 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
Only Terasic DE10-Agilex Development Board (Agilex 7, FPGA AI Suite) has published object detection results.
| Platform | Result | Model · precision · stack | Grade | Power | Source |
|---|---|---|---|---|---|
| Terasic DE10-Agilex Development Board (Agilex 7, FPGA AI Suite) | 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) |
Google Coral USB Accelerator (Edge TPU) on Xeon Gold 6154 host, USB 3.0:
No published object detection results for Google Coral USB Accelerator (Edge TPU) on Xeon Gold 6154 host, USB 3.0 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.
instance segmentation
No published instance segmentation results for Google Coral USB Accelerator (Edge TPU) on Xeon Gold 6154 host, USB 3.0 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 Google Coral USB Accelerator (Edge TPU) on Xeon Gold 6154 host, USB 3.0 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
Only Terasic DE10-Agilex Development Board (Agilex 7, FPGA AI Suite) has published image classification results.
| 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) |
Google Coral USB Accelerator (Edge TPU) on Xeon Gold 6154 host, USB 3.0:
No published image classification results for Google Coral USB Accelerator (Edge TPU) on Xeon Gold 6154 host, USB 3.0 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.
semantic segmentation
| Platform | Result | Model · precision · stack | Grade | Power | Source |
|---|---|---|---|---|---|
| Google Coral USB Accelerator (Edge TPU) on Xeon Gold 6154 host, USB 3.0 | 303 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) |
| 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) |
speech-to-text
No published speech-to-text results for Google Coral USB Accelerator (Edge TPU) on Xeon Gold 6154 host, USB 3.0 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 Google Coral USB Accelerator (Edge TPU) on Xeon Gold 6154 host, USB 3.0 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.
Split result: Google Coral USB Accelerator (Edge TPU) on Xeon Gold 6154 host, USB 3.0 is faster in 1 and Terasic DE10-Agilex Development Board (Agilex 7, FPGA AI Suite) in 3 of 4 like-for-like comparisons.
| Google Coral USB Accelerator (Edge TPU) on Xeon Gold 6154 host, USB 3.0 | Terasic DE10-Agilex Development Board (Agilex 7, FPGA AI Suite) | |
|---|---|---|
| Best image classification | 476 FPS D Older data (2019) | 1391 FPS D 2026-08-10 (published) |
| Power | 2.00 W spec | n/a |
| Perf / W | 238 FPS/W spec | n/a |
| Price | n/a | n/a |
| Workloads with data | 3 of 7 | 3 of 7 |
Best published image classification 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
| Platform | Result | Model · precision · stack | Grade | Power | Source |
|---|---|---|---|---|---|
| Google Coral USB Accelerator (Edge TPU) on Xeon Gold 6154 host, USB 3.0 | 154 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) |
| Terasic DE10-Agilex Development Board (Agilex 7, FPGA AI Suite) MIXED: model-size class unknown vs mobile-class CNN | 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 Google Coral USB Accelerator (Edge TPU) on Xeon Gold 6154 host, USB 3.0 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 Google Coral USB Accelerator (Edge TPU) on Xeon Gold 6154 host, USB 3.0 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) | 220 FPS throughput · b=1 | resnet-50-tf 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) |
| Google Coral USB Accelerator (Edge TPU) on Xeon Gold 6154 host, USB 3.0 | 20.41 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) |
| 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) |
| Google Coral USB Accelerator (Edge TPU) on Xeon Gold 6154 host, USB 3.0 | 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) |
| Platform | Result | Model · precision · stack | Grade | Power | Source |
|---|---|---|---|---|---|
| Terasic DE10-Agilex Development Board (Agilex 7, FPGA AI Suite) | 1289 FPS throughput · b=1 | yolo-v8-nano classification not stated in table (set by .arch file) · 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) |
| Google Coral USB Accelerator (Edge TPU) on Xeon Gold 6154 host, USB 3.0 | 476 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) |
semantic segmentation
| Platform | Result | Model · precision · stack | Grade | Power | Source |
|---|---|---|---|---|---|
| Google Coral USB Accelerator (Edge TPU) on Xeon Gold 6154 host, USB 3.0 | 303 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) |
| 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) |
speech-to-text
No published speech-to-text results for Google Coral USB Accelerator (Edge TPU) on Xeon Gold 6154 host, USB 3.0 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 Google Coral USB Accelerator (Edge TPU) on Xeon Gold 6154 host, USB 3.0 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.