4 papers
SASQ: Static Activation Scaling for Quantization-Aware Training in Large Language Models
Shizhuo Mao, Song Chen, Yi Kang
Large language models (LLMs) excel at natural language tasks but face deployment challenges due to their growing size outpacing GPU memory advancements. Model quantization mitigate…
HQ-DM: Single Hadamard Transformation-Based Quantization-Aware Training for Low-Bit Diffusion Models
Shizhuo Mao, Hongtao Zou, Qihu Xie +2
Diffusion models have demonstrated significant applications in the field of image generation. However, their high computational and memory costs pose challenges for deployment. Mod…
YOCO: A Hybrid In-Memory Computing Architecture with 8-bit Sub-PetaOps/W In-Situ Multiply Arithmetic for Large-Scale AI
Zihao Xuan, Yuxuan Yang, Wei Xuan +3
In this paper, we further explore the potential of analog in-memory computing (AiMC) and introduce an innovative artificial intelligence (AI) accelerator architecture named YOCO, f…
SparseMap: Loop Mapping for Sparse CNNs on Streaming Coarse-grained Reconfigurable Array
Xiaobing Ni, Mengke Ge, Jiaheng Ruan +2
Streaming coarse-grained reconfgurable array (CGRA) is a promising architecture for data/computing-intensive applications because of its fexibility, high throughput and efcient mem…