pith:XDHKGM5I
{\mu}-ORCA: Optimizing Acceleration for Microsecond-Scale Deep Neural Network Inference on ACAP
μ-ORCA achieves 0.93 μs DNN inference latency on ACAP by direct AIE array communication
arxiv:2605.17683 v1 · 2026-05-17 · cs.AR
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μ-ORCA achieves 0.93 μs latency for a 6-layer real-world DeepSets model, satisfying the 1-μs latency budget, with average latency reduction of >1.70× and >1.83× compared with different state-of-the-art ACAP frameworks.
The assumption that direct inter-layer communication on the AIE array can be realized with negligible additional synchronization or routing overhead for the small problem sizes typical of jet-tagging models (section on framework design and evaluation).
μ-ORCA achieves 0.93 μs end-to-end latency for a 6-layer DeepSets model on AMD ACAP VEK280 by direct AIE-array inter-layer communication and overhead-aware design space exploration, delivering over 1.7× latency reduction versus prior ACAP frameworks.
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| First computed | 2026-05-20T00:04:52.577087Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
b8cea333a842f5f6f96843067c186650dd667fcc39be57a3d0be7840a1ece6e3
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curl -sH 'Accept: application/ld+json' https://pith.science/pith/XDHKGM5IIL27N6LIIMDHYGDGKD \
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Canonical record JSON
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