基于单片三维集成的Transformer加速器架构

By employing a monolithic 3D integrated architecture in the Transformer accelerator, stacking silicon-based logic circuits, RRAM-CIM arrays, and CFET 2TOC-CIM arrays, and utilizing high-density interlayer dielectric vias for inter-module communication, the problem of data transport limitations is solved, enabling more efficient Transformer computation.

CN118468950BActive Publication Date: 2026-07-17TSINGHUA UNIVERSITY

Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
TSINGHUA UNIVERSITY
Filing Date
2024-05-14
Publication Date
2026-07-17

AI Technical Summary

Technical Problem

In the existing Transformer accelerator architecture, data transfer between fully static and semi-static weight modules limits system performance, making it difficult to completely overcome the von Neumann bottleneck and the slowdown of Moore's Law.

Method used

Employing a monolithically integrated Transformer accelerator architecture, this system utilizes a stack of silicon-based logic circuits, RRAM-CIM arrays, and CFET 2TOC-CIM arrays. High-density interlayer dielectric vias are used for inter-module communication, leveraging the advantages of different in-memory computing methods to achieve linear transformations and matrix multiplication operations.

Benefits of technology

It greatly reduces the amount of data transfer, overcomes the von Neumann bottleneck, continues Moore's Law, and achieves better Transformer acceleration.

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Abstract

本发明涉及人工智能算法硬件加速技术领域,特别涉及一种基于单片三维集成的Transformer加速器架构,包括:硅基逻辑电路,用于根据目标自然语言处理任务控制目标Transformer的电流流动方向进行逻辑运算;RRAM‑CIM阵列,其堆叠在硅基逻辑电路上方并互连通信,用于作为全静态权重模块,以根据目标自然语言处理任务执行线性变换运算;CFET 2T0C‑CIM阵列,其堆叠在RRAM‑CIM阵列上方并互连通信,用于作为半静态权重模块,以根据目标自然语言处理任务执行矩阵乘法运算。由此,解决了现有Transformer加速器结构中的全静态权重模块和半静态权重模块之间的数据搬运会限制系统性能等问题。
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