Hardware-Software Co-Design Accelerates Multimodal Foundation Models
A novel approach to enhancing multimodal foundation models (MFMs) integrates the co-design of transformer blocks in both hardware and software with an optimization pipeline aimed at minimizing memory and computational demands. This strategy encompasses domain-specific fine-tuning, mixed-precision quantization that considers hierarchy, structural pruning, speculative decoding, and model cascading featuring lightweight self-tests. Additionally, it involves the co-optimization of sequence length, visual resolution, stride, and the fusion of graph-level operators. The findings are presented in arXiv:2604.21952.
Key facts
- Methodology combines hardware and software co-design of transformer blocks.
- Uses hierarchy-aware mixed-precision quantization and structural pruning.
- Employs speculative decoding and model cascading with lightweight self-tests.
- Co-optimizes sequence length, visual resolution, stride, and graph-level operator fusion.
- Published on arXiv with ID 2604.21952.
- Focuses on accelerating multimodal foundation models (MFMs).
- Includes fine-tuning for domain-specific adaptation.
- Reduces computational and memory requirements.
Entities
Institutions
- arXiv