37 citations · 114 across the 14 of their papers we have counts for
4 papers · 1 filter
Scaling Deep Learning Computation over the Inter-Core Connected Intelligence Processor with T10
Yiqi Liu, Yuqi Xue, Yu Cheng +4
As AI chips incorporate numerous parallelized cores to scale deep learning (DL) computing, inter-core communication is enabled recently by employing high-bandwidth and low-latency…
LUT Tensor Core: A Software-Hardware Co-Design for LUT-Based Low-Bit LLM Inference
Zhiwen Mo, Lei Wang, Jianyu Wei +8
Large Language Model (LLM) inference becomes resource-intensive, prompting a shift toward low-bit model weights to reduce the memory footprint and improve efficiency. Such low-bit…
T-MAC: CPU Renaissance via Table Lookup for Low-Bit LLM Deployment on Edge
Jianyu Wei, Shijie Cao, Ting Cao +4
The deployment of Large Language Models (LLMs) on edge devices is increasingly important to enhance on-device intelligence. Weight quantization is crucial for reducing the memory f…
The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits
Shuming Ma, Hongyu Wang, Lingxiao Ma +7
Recent research, such as BitNet, is paving the way for a new era of 1-bit Large Language Models (LLMs). In this work, we introduce a 1-bit LLM variant, namely BitNet b1.58, in whic…