The paper compiles hardware-software co-design techniques including mixed-precision quantization, structural pruning, speculative decoding, and transformer accelerators to speed up multimodal foundation models, with examples in medical and code tasks.
Dynamic context pruning for efficient and interpretable autoregressive transformers
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Focus Session: Hardware and Software Techniques for Accelerating Multimodal Foundation Models
The paper compiles hardware-software co-design techniques including mixed-precision quantization, structural pruning, speculative decoding, and transformer accelerators to speed up multimodal foundation models, with examples in medical and code tasks.