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

A new paper from researchers at NYU Abu Dhabi and TU Wien introduces a comprehensive methodology for accelerating multimodal foundation models (MFMs) through hardware-software co-design. The work, accepted at DATE 2026 in Verona, combines hierarchy-aware mixed-precision quantization and structural pruning for transformer blocks and MLP channels to reduce computational and memory requirements. It also employs speculative decoding, model cascading that routes queries through a small-to-large cascade using lightweight self-tests, and co-optimization of sequence length, visual resolution, and graph-level operator fusion. To execute efficiently, the processing dataflow is optimized for underlying hardware with memory-efficient attention to meet on-chip bandwidth and latency budgets, using a specialized hardware accelerator for transformer workloads developed through expert design or an LLM-aided approach.

The methodology's effectiveness was demonstrated on medical-MFMs and code generation tasks, showing significant improvements in both speed and energy efficiency. The paper concludes with extensions toward energy-efficient spiking-MFMs, which mimic biological neural networks for even lower power consumption. This multi-layered approach addresses critical bottlenecks in deploying large multimodal models on resource-constrained devices, such as edge servers or mobile hardware. By combining software optimizations like pruning and quantization with custom hardware design, the researchers aim to make advanced AI accessible for real-time applications in healthcare, robotics, and autonomous systems, where low latency and limited power budgets are essential.