2 citations · 2 across the 2 of their papers we have counts for
2 papers
cs.LG2026
InnerQ: Hardware-Aware Tuning-Free Quantization of KV Cache for Large Language Models
Sayed Mohammadreza Tayaranian Hosseini, Amir Ardakani, Warren J. Gross
When transformer-based language models are deployed for text generation, most of the inference time is spent in the decoding stage, where output tokens are generated sequentially.…
cs.LG2022★ 2 cited
Standard Deviation-Based Quantization for Deep Neural Networks
Amir Ardakani, Arash Ardakani, Brett Meyer +2
Quantization of deep neural networks is a promising approach that reduces the inference cost, making it feasible to run deep networks on resource-restricted devices. Inspired by ex…