19 citations · 20 across the 6 of their papers we have counts for
6 papers
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.…
Memory-Efficient FPGA Implementation of Stochastic Simulated Annealing
Duckgyu Shin, Naoya Onizawa, Warren J. Gross +1
Simulated annealing (SA) is a well-known algorithm for solving combinatorial optimization problems. However, the computation time of SA increases rapidly, as the size of the proble…
SSS3D: Fast Neural Architecture Search For Efficient Three-Dimensional Semantic Segmentation
Olivier Therrien, Marihan Amein, Zhuoran Xiong +2
We present SSS3D, a fast multi-objective NAS framework designed to find computationally efficient 3D semantic scene segmentation networks. It uses RandLA-Net, an off-the-shelf poin…
FMAS: Fast Multi-Objective SuperNet Architecture Search for Semantic Segmentation
Zhuoran Xiong, Marihan Amein, Olivier Therrien +2
We present FMAS, a fast multi-objective neural architecture search framework for semantic segmentation. FMAS subsamples the structure and pre-trained parameters of DeepLabV3+, with…
BD-KD: Balancing the Divergences for Online Knowledge Distillation
Ibtihel Amara, Nazanin Sepahvand, Brett H. Meyer +2
We address the challenge of producing trustworthy and accurate compact models for edge devices. While Knowledge Distillation (KD) has improved model compression in terms of achievi…
CES-KD: Curriculum-based Expert Selection for Guided Knowledge Distillation
Ibtihel Amara, Maryam Ziaeefard, Brett H. Meyer +2
Knowledge distillation (KD) is an effective tool for compressing deep classification models for edge devices. However, the performance of KD is affected by the large capacity gap b…