arXiv subjects

Rongqing Cong

Publications and source records attributed to Rongqing Cong.

2 recordsLinked to original sources

arXiv2026

A Magnon-Based Electric Field Controlled Magnetoelectric Device for Energy-Efficient Logic-in-Memory

We demonstrate a non-volatile magnetoelectric magnonic memory (MEMM) that enables fully electrical write/read via direct magnon-driven sensing in an insulating antiferromagnet. A fabricated SrIrO3/La-BiFeO3/SrIrO3 trilayer exhibits sub-100 ps switching, a remnant polarization of 20 uC/cm2, and a readout voltage contrast close to 1mV between high and low-resistance states. To connect device physics to circuit behavior, we develop and experimentally validate a compact circuit model that captures spin Hall injection and spin transport. Simulations with optimized material parameters predict output voltages > 100mV, enabling cascading without external amplification. Using this framework, we design MEMM-based memory and logic blocks, including a 1T1R array, two inverter implementations (complementary two-device and single-device), and a three-input majority gate, and evaluate deep-pipelined operation. The model projects switching energies down to 1 aJ per operation and logic propagation delays of 30-60 ps, indicating MEMM as a promising platform for energy-constrained, high throughput computing.

cond-mat.mtrl-sci↗

arXiv2024

AttentionLego: An Open-Source Building Block For Spatially-Scalable Large Language Model Accelerator With Processing-In-Memory Technology

Large language models (LLMs) with Transformer architectures have become phenomenal in natural language processing, multimodal generative artificial intelligence, and agent-oriented artificial intelligence. The self-attention module is the most dominating sub-structure inside Transformer-based LLMs. Computation using general-purpose graphics processing units (GPUs) inflicts reckless demand for I/O bandwidth for transferring intermediate calculation results between memories and processing units. To tackle this challenge, this work develops a fully customized vanilla self-attention accelerator, AttentionLego, as the basic building block for constructing spatially expandable LLM processors. AttentionLego provides basic implementation with fully-customized digital logic incorporating Processing-In-Memory (PIM) technology. It is based on PIM-based matrix-vector multiplication and look-up table-based Softmax design. The open-source code is available online: https://bonany.cc/attentionleg.

cs.AR↗