arXiv:2603.22867v1 Announce Type: cross Abstract: Multimodal stacks that mix ViTs, CNNs, GNNs, and transformer NLP strain embedded platforms because their compute/memory patterns diverge and hard real-time targets leave little slack. TRINE is a single-bitstream FPGA accelerator and compiler that executes end-to-end multimodal inference without reconfiguration. Layers are unified as DDMM/SDDMM/SpMM and mapped to a mode-switchable engine that toggles at runtime among weight/output-stationary systolic, 1xCS SIMD, and a routable adder tree (RADT) on a shared PE array. A width-matched, two-stage to
The rapid development of multimodal AI architectures is creating severe bottlenecks for embedded deployment, driving demand for innovative and efficient hardware solutions.
This development represents a significant step towards enabling powerful multimodal AI inference on resource-constrained embedded platforms, expanding AI's reach and applications.
Hardware for multimodal AI inference can now be more flexible, efficient, and runtime-adaptive, unifying diverse compute patterns on a single FPGA accelerator without reconfiguration.
- · AI hardware developers
- · Embedded systems industry
- · Edge AI applications
- · Multimodal AI research
- · ASIC-only custom silicon developers
- · Inefficient AI deployment strategies
TRINE offers a more efficient and adaptable platform for deploying complex multimodal AI models on edge devices.
This efficiency could accelerate the development and adoption of advanced AI in autonomous systems, robotics, and industrial IoT.
Reduced computational overhead could lower the energy footprint of advanced AI, potentially impacting the broader energy demands of AI infrastructure.
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Read at arXiv cs.LG