4 papers
Trellis: Learning to Compress Key-Value Memory in Attention Models
Mahdi Karami, Ali Behrouz, Praneeth Kacham +1
Transformers, while powerful, suffer from quadratic computational complexity and the ever-growing Key-Value (KV) cache of the attention mechanism. This paper introduces Trellis, a…
Approximating the Top Eigenvector in Random Order Streams
Praneeth Kacham, David P. Woodruff
When rows of an matrix are given in a stream, we study algorithms for approximating the top eigenvector of the matrix (equivalently, the top right singula…
LevAttention: Time, Space, and Streaming Efficient Algorithm for Heavy Attentions
Ravindran Kannan, Chiranjib Bhattacharyya, Praneeth Kacham +1
A central problem related to transformers can be stated as follows: given two matrices and , and a non-negative function , define the matrix as follows:…
Faster Algorithms for Schatten-p Low Rank Approximation
Praneeth Kacham, David P. Woodruff
We study algorithms for the Schatten- Low Rank Approximation (LRA) problem. First, we show that by using fast rectangular matrix multiplication algorithms and different block si…