arXiv: 2604.21952 · PDF

Authors: Muhammad Shafique, Abdul Basit, Muhammad Abdullah Hanif, Alberto Marchisio, Rachmad Vidya Wicaksana Putra, Minghao Shao

Affiliations: New York University Abu Dhabi

Primary category: cs.LG · all: cs.AI, cs.AR, cs.LG, cs.NE, cs.RO

Matched keywords: llm, quantization, speculative decoding, attention, transformer, latency, fine-tun

TL;DR

A multi-layered hardware/software co-design methodology to accelerate multimodal foundation models (MFMs), combining compression, execution-level optimizations, and specialized accelerators, demonstrated on medical-MFMs and code generation, with extensions toward spiking-MFMs.

Key Ideas

  • End-to-end optimization pipeline spanning model development, compression, execution, and hardware.
  • Hierarchy-aware mixed-precision quantization + structural pruning of transformer blocks and MLP channels.
  • Speculative decoding and small-to-large model cascading with lightweight self-tests for escalation.
  • Co-optimization of sequence length, visual resolution/stride, and graph-level operator fusion.
  • Dataflow and memory-efficient attention tuned to on-chip bandwidth/latency budgets.
  • Specialized transformer accelerator via expert or LLM-aided design.
  • Extension path toward energy-efficient spiking-MFMs.

Approach

Fine-tuning adapts MFMs to target domains. Models are compressed through hierarchy-aware mixed-precision quantization and structural pruning. Execution is accelerated with speculative decoding, cascaded routing (small→large with self-tests), and joint tuning of sequence length, visual resolution, stride, and operator fusion. The runtime uses memory-efficient attention and a dataflow matched to the target accelerator. A custom transformer accelerator—built by experts or with LLM-aided design—executes the workloads.

Experiments

The abstract is thin on specifics: it names medical-MFMs and code generation as demonstration domains but does not list datasets, baselines, or quantitative metrics.

Results

No headline numbers are given in the abstract; it only claims effectiveness on medical-MFMs and code generation and forward-looking extensions toward spiking-MFMs. Claims cannot be independently verified from the abstract alone.

Why It Matters

Practitioners deploying MFMs on constrained hardware get a consolidated playbook: compression + cascading + dataflow-aware execution + custom silicon. The cascade-with-self-test pattern is attractive for cost control, and the LLM-aided accelerator design hints at faster hardware iteration cycles.

Connections to Prior Work

Builds on mixed-precision quantization and structural pruning for transformers, speculative decoding (e.g., draft/verifier schemes), model cascading/routing, FlashAttention-style memory-efficient attention, graph-level fusion (XLA/TVM lineage), and specialized transformer accelerators. The spiking-MFM direction connects to neuromorphic SNN research; LLM-aided hardware design echoes recent EDA-with-LLMs work.

Open Questions

  • Which datasets, baselines, and metrics quantify the claimed gains?
  • How do compression + cascading interact—does pruning degrade the small-model accuracy that the self-test relies on?
  • What is the accuracy/latency/energy Pareto on real silicon versus GPUs?
  • How reliable are the lightweight self-tests at deciding escalation?
  • How mature is the LLM-aided accelerator design path compared to expert design?
  • What concrete architecture and training recipe enables the spiking-MFM extension?

Original 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.