1 citations · 1 across the 2 of their papers we have counts for
4 papers
31.1 A 14.08-to-135.69Token/s ReRAM-on-Logic Stacked Outlier-Free Large-Language-Model Accelerator with Block-Clustered Weight-Compression and Adaptive Parallel-Speculative-Decoding
Pingcheng Dong, Yonghao Tan, Xuejiao Liu +13
This work presents a 55nm speculative decoding-based LLM accelerator with bumping-based face-to-face ReRAM-on-logic stacking technology. It features a local rotation unit for outli…
EoRA: Fine-tuning-free Compensation for Compressed LLM with Eigenspace Low-Rank Approximation
Shih-Yang Liu, Maksim Khadkevich, Nai Chit Fung +10
While post-training compression techniques effectively reduce the memory footprint, latency, and power consumption of Large Language Models (LLMs), they often result in noticeable…
A 28nm 0.22μJ/token memory-compute-intensity-aware CNN-Transformer accelerator with hybrid-attention-based layer-fusion and cascaded pruning for semantic-segmentation
Pingcheng Dong, Yonghao Tan, Xuejiao Liu +14
This work presents a 28nm 13.93mm2 CNN-Transformer accelerator for semantic segmentation, achieving 3.86-to-10.91x energy reduction over previous designs. It features a hybrid atte…
APSQ: Additive Partial Sum Quantization with Algorithm-Hardware Co-Design
Yonghao Tan, Pingcheng Dong, Yongkun Wu +8
DNN accelerators, significantly advanced by model compression and specialized dataflow techniques, have marked considerable progress. However, the frequent access of high-precision…