Going Beyond the Edge: Distributed Inference of Transformer Models on Ultra-Low-Power Wireless Devices
Transformer models are rapidly becoming a cornerstone of modern Internet of Things (IoT) applications, yet their computational and memory demands far exceed the capabilities of a single typical ultra-low-power IoT device. We present CATS, a framework for distributed transformer inference on ultra-low-power wireless devices, enabling multiple devices to collaboratively execute models far larger than what a single device can sustain. At its core, CATS is a communication-aware distributed transformer inference scheme co-designed across transformer partitioning, wireless communication and training. It employs SomeGather, a new pruned communication primitive that selectively broadcasts activat...
| Authors | Alexander Gräfe, Ding Huo, Vincent de Bakker et al. |
|---|---|
| Primary category | cs.LG |
| Published | 15 May 2026 |
| Last updated at source | 18 May 2026 |
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