Focus Session: Hardware and Software Techniques for Accelerating Multimodal Foundation Models
Muhammad Shafique, Abdul Basit, Muhammad Abdullah Hanif, Alberto Marchisio, Rachmad Vidya Wicaksana Putra, Minghao Shao
Why It Matters
What makes this one worth your time
This work is relevant for AI engineers and researchers looking to improve the efficiency and performance of multimodal foundation models, which are increasingly used in diverse applications.
A comprehensive approach to accelerate multimodal foundation models through hardware-software co-design and optimization techniques.
Summary
The paper presents a methodology for accelerating multimodal foundation models by integrating hardware and software co-design with optimization techniques. It includes MFM compression, speculative decoding, model cascading, and co-optimization of various parameters, supported by a specialized hardware accelerator.
Key contributions
- Introduction of a multi-layered methodology for MFM acceleration.
- Development of a specialized hardware accelerator for transformer workloads.
- Implementation of MFM compression and optimization techniques.
Notable insights
- The use of hierarchy-aware mixed-precision quantization and structural pruning for transformer blocks and MLP channels.
- Speculative decoding and model cascading to optimize operations and resource allocation.
Possible limitations
- Not stated in the abstract
Abstract
arXiv:2604.21952v1 Announce Type: cross Abstract: This work presents a multi-layered methodology for efficiently accelerating multimodal foundation models (MFMs). It combines hardware and software co-design of transformer blocks with an optimization pipeline that reduces computational and memory requirements. During model development, it employs performance enhancements through fine-tuning for domain-specific adaptation. Our methodology further incorporates hardware and software techniques for optimizing MFMs. Specifically, it employs MFM compression using hierarchy-aware mixed-precision quantization and structural pruning for transformer blocks and MLP channels. It also optimizes operations through speculative decoding, model cascading that routes queries through a small-to-large cascade and uses lightweight self-tests to determine when to escalate to larger models, as well as co-optimization of sequence length, visual resolution & stride, and graph-level operator fusion. To efficiently execute the model, the processing dataflow is optimized based on the underlying hardware architecture together with memory-efficient attention to meet on-chip bandwidth and latency budgets. To support this, a specialized hardware accelerator for the transformer workloads is employed, which can be developed through expert design or an LLM-aided design approach. We demonstrate the effectiveness of the proposed methodology on medical-MFMs and on code generation tasks, and conclude with extensions toward energy-efficient spiking-MFMs.