Shaojun Wei — ISSCC 知识库

ISSCC 2025 Session 14 AI / ML

A 51.6TFLOPs/W Full-Datapath CIM Macro Approaching Sparsity Bound and <2-30 Loss for Compound AI

Zhiheng Yue*, Xujiang Xiang*, Yang Wang, Ruiqi Guo, Huiming Han,

with exceptional performance, but their prohibitive size and cost limits deployment on edge devices. The compound-AI combines several specialized small models to achieve matched or even superior accuracy on target downst

Shaojun Wei, Yang Hu, Shouyi Yin Tsinghua University, Beijing, China *

ISSCC 2023 Session 16 Digital Processors

TensorCIM: A 28nm 3.7nJ/Gather and 8.3TFLOPS/W FP32 Digital-CIM Tensor Processor for MCM-CIM-Based Beyond-NN Acceleration

Fengbin Tu, Yiqi Wang, Zihan Wu, Weiwei Wu, Leibo Liu, Yang Hu,

Recommendation Models (DLRMs) have computational and data-movement requirements beyond those seen in typical NN processing. Such beyond-NN applications typically consist of Sparse Gathering (SpG) and Sparse Algebra (SpA)

Shaojun Wei, Shouyi Yin Tsinghua University, Beijing, China Applicatio

ISSCC 2023 Session 16 AI / ML

MulTCIM: A 28nm 2.24µJ/Token Attention-Token-Bit Hybrid Sparse Digital CIM-Based Accelerator for Multimodal Transformers

Fengbin Tu, Zihan Wu, Yiqi Wang, Weiwei Wu, Leibo Liu, Yang Hu,

natural language, speech, etc. Multimodal Transformer (MulT, Fig. 16.1.1) models introduce a cross-modal attention mechanism to vanilla transformers to learn from different modalities, achieving excellent results on mult

Shaojun Wei, Shouyi Yin Tsinghua University, Beijing, China Human perc