5 papers · 1 filter
Sparse Attention as a Range Searching Problem: Towards an Inference-Efficient Index for KV Cache
Mohsen Dehghankar, Abolfazl Asudeh
Sparse attention improves LLM inference efficiency by selecting a subset of key-value entries, but at the cost of potential accuracy degradation. In particular, omitting critical K…
Rank It, Then Ask It: Input Reranking for Maximizing the Performance of LLMs on Symmetric Tasks
Mohsen Dehghankar, Abolfazl Asudeh
Large language models (LLMs) have quickly emerged as practical and versatile tools that provide new solutions for a wide range of domains. In this paper, we consider the applicatio…
An Efficient Matrix Multiplication Algorithm for Accelerating Inference in Binary and Ternary Neural Networks
Mohsen Dehghankar, Mahdi Erfanian, Abolfazl Asudeh
Despite their tremendous success and versatility, Deep Neural Networks (DNNs) such as Large Language Models (LLMs) suffer from inference inefficiency and rely on advanced computati…
Mining the Minoria: Unknown, Under-represented, and Under-performing Minority Groups
Mohsen Dehghankar, Abolfazl Asudeh
Due to a variety of reasons, such as privacy, data in the wild often misses the grouping information required for identifying minorities. On the other hand, it is known that machin…
[Experiments & Analysis] Evaluating the Feasibility of Sampling-Based Techniques for Training Multilayer Perceptrons
Sana Ebrahimi, Rishi Advani, Abolfazl Asudeh
The training process of neural networks is known to be time-consuming, and having a deep architecture only aggravates the issue. This process consists mostly of matrix operations,…