Advances in Accelerating Large Language Models

The field of large language models is moving towards accelerating training and inference times, with a focus on innovative techniques such as sparsity, parallelization, and adaptive rank allocation. Researchers are exploring hardware-accelerated approaches, including FPGA-based accelerators and SmartNICs, to improve performance and reduce latency. Additionally, novel architectural optimization techniques, such as FFN Fusion, are being developed to reduce sequential computation and improve inference efficiency. Noteworthy papers include:

  • Accelerating Transformer Inference and Training with 2:4 Activation Sparsity, which demonstrates the potential for sparsity to play a key role in accelerating large language model training and inference.
  • FFN Fusion: Rethinking Sequential Computation in Large Language Models, which introduces an architectural optimization technique that reduces sequential computation in large language models by identifying and exploiting natural opportunities for parallelization.

Sources

Accelerating Transformer Inference and Training with 2:4 Activation Sparsity

Design and Implementation of an FPGA-Based Tiled Matrix Multiplication Accelerator for Transformer Self-Attention on the Xilinx KV260 SoM

Reliable Replication Protocols on SmartNICs

Adaptive Rank Allocation: Speeding Up Modern Transformers with RaNA Adapters

ZeroLM: Data-Free Transformer Architecture Search for Language Models

FFN Fusion: Rethinking Sequential Computation in Large Language Models

An Efficient Data Reuse with Tile-Based Adaptive Stationary for Transformer Accelerators

SemEval-2025 Task 9: The Food Hazard Detection Challenge

UB-Mesh: a Hierarchically Localized nD-FullMesh Datacenter Network Architecture

Neural Architecture Search by Learning a Hierarchical Search Space

Arch-LLM: Taming LLMs for Neural Architecture Generation via Unsupervised Discrete Representation Learning

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