Sparse by Command: Task-Conditional Compute Skipping for Multi-Task Inference Accelerators
Multi-task inference models share a single backbone across diverse tasks, yet execute identical computation regardless of which task is active - wasting energy and cycles on task-irrelevant operations. We observe that the task command, typically available before inference begins, provides a free signal that can be exploited to skip unnecessary computation at the hardware level. We present a HW/SW co-designed approach in which a lightweight gating network, trained jointly with the backbone, predicts per-tile binary execution masks conditioned on the task input. Each tile corresponds to a fixed group of output channels (the native scheduling granularity of the accelerator), enabling masked ...
| Authors | Afzal Ahmad, Gaoyu Mao, Shoubo Hu et al. |
|---|---|
| Primary category | cs.AR |
| Published | 24 July 2026 |
| Last updated at source | 24 July 2026 |
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