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Mission

Mission

Run a mission workload on connected hardware.

Workload
mapping model to chip
CHIP SEES
—
teach an object to begin
ImagesSelect an image

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▶ ▦ FRAMES
THR 0.30

Outline: selected✓ Processed+ Positive screening result! Partial or failed run

+ TEACH ⟲ RESET
– recorded runs
Run history built for honest hardware comparisons.
Every explicit run records its workload, execution target, timing, throughput, power, energy, and evidence source when those measurements exist. Missing values stay blank.
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Filtered runs
0
Explicit hardware executions in this view.
Best throughput
-- fps
Only from rows that reported measured throughput.
Lowest energy
-- mJ
Only comparable within the same workload and input mode.
Comparison rule. Compare hardware only when workload, model, input set, and execution mode match. Dual rows report aggregate throughput across independent partitions. Apex currently runs its vendor COCO reference detector, so it is not an accuracy comparison with the satellite models.
FPGA outputs
Every image here was boxed by LocateAnything 3B on the real Apex FPGA. Timing, power, and satellite accuracy are not claimed on this page.
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Benchmarks

Hardware benchmarks

Results are grouped by processor type. Each figure is marked as measured by us, quoted from the vendor, or derived from a stated reference. Anything we have not measured is left blank.

Recorded hardware runs, vendor specifications and reference assumptions are marked below. Power boundaries vary by hardware; these values do not establish a whole-system power advantage. * Selected for the flight design describes the intended role, not flight qualification.

Measured by us on the hardwareVendor specificationReference operating assumptionBlank not measured

Neuromorphic processors

Real Akida silicon, including the on-device learning capability that separates this architecture.

Conventional & programmable accelerators

GPU, FPGA, and AI-accelerator evidence shown separately from neuromorphic results.

On-chip learning. Few-shot learning during inference has been demonstrated on the AKD1000. The current learning model needs 46 neural processors, so it does not fit the 32-NP AKD1500 yet.
AKD1500 energy. The AKD1500 does not report power. Energy per frame uses a 250 mW-per-chip reference divided by measured throughput. This is an assumption, not telemetry.
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