Karthikeyan Sankaralingam — accepted AI-conference papers

2024

IM-Unpack: Training and Inference with Arbitrarily Low Precision Integers

Zhanpeng Zeng, Karthikeyan Sankaralingam, Vikas Singh

ICML 2024poster

GEneral Matrix Multiply (GEMM) is a central operation in deep learning and corresponds to a large chunk of the compute footprint. Therefore, improving its efficiency is an active topic of research. A popular strategy is the use of low bit-width integers to approximate the original matrix entries. Th…

Cited by 1 Source ↗PDF ↗code ↗Scholar ↗

2023

LookupFFN: Making Transformers Compute-lite for CPU inference

Zhanpeng Zeng, Michael Davies, Pranav Pulijala, Karthikeyan Sankaralingam, Vikas Singh

ICML 2023poster

While GPU clusters are the de facto choice for training large deep neural network (DNN) models today, several reasons including ease of workflow, security and cost have led to efforts investigating whether CPUs may be viable for inference in routine use in many sectors of the industry. But the imbal…

Cited by 7 Source ↗PDF ↗code ↗Scholar ↗