5 papers
Spark Transformer: Reactivating Sparsity in FFN and Attention
Chong You, Kan Wu, Zhipeng Jia +16
The discovery of the lazy neuron phenomenon in trained Transformers, where the vast majority of neurons in their feed-forward networks (FFN) are inactive for each token, has spurre…
A Faster Generalized Two-Stage Approximate Top-K
Yashas Samaga, Varun Yerram, Spandana Raj Babbula +2
We consider the Top- selection problem, which aims to identify the largest elements in an array. Top- selection arises in many machine learning algorithms and often becom…
HiRE: High Recall Approximate Top- Estimation for Efficient LLM Inference
Yashas Samaga B L, Varun Yerram, Chong You +4
Autoregressive decoding with generative Large Language Models (LLMs) on accelerators (GPUs/TPUs) is often memory-bound where most of the time is spent on transferring model paramet…
Tandem Transformers for Inference Efficient LLMs
Aishwarya P S, Pranav Ajit Nair, Yashas Samaga +4
The autoregressive nature of conventional large language models (LLMs) inherently limits inference speed, as tokens are generated sequentially. While speculative and parallel decod…
Making the Last Iterate of SGD Information Theoretically Optimal
Prateek Jain, Dheeraj Nagaraj, Praneeth Netrapalli
Stochastic gradient descent (SGD) is one of the most widely used algorithms for large scale optimization problems. While classical theoretical analysis of SGD for convex problems s…