Focus Session: Hardware and Software Techniques for Accelerating Multimodal Foundation Models

2026-04-23

AI Summary

This work addresses the challenge of efficiently deploying multimodal foundation models, which are hindered by substantial computational and memory overhead. The authors propose a hardware-software co-design framework that integrates layer-aware mixed-precision quantization, structured pruning, speculative decoding, and a model cascading routing mechanism. This approach jointly optimizes sequence length, visual resolution, and operator graph structure, while introducing a memory-efficient attention scheme and a customized Transformer hardware accelerator. Evaluated on medical multimodal understanding and code generation tasks, the method significantly reduces resource consumption. Furthermore, it is extended to energy-efficient spiking multimodal models, demonstrating broad applicability and effectiveness across diverse architectures.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Computer Vision: Multi-modal Vision
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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.

Problem

• multimodal foundation models
• model acceleration
• hardware-software co-design
• computational efficiency
• memory optimization

Innovation

• multimodal foundation models
• hardware-software co-design
• mixed-precision quantization
• speculative decoding
• model cascading