Core Viewpoint - The article discusses the development of a novel 2T1M optoelectronic hybrid computing architecture that addresses the IR drop issue in traditional CIM architectures, enabling larger array sizes and improved performance for deep learning applications, particularly in large-scale Transformer models [1][2][9]. Group 1: Architecture Design and Working Principle - The 2T1M architecture integrates electronic and photonic technologies to mitigate IR drop issues, utilizing a combination of two transistors and a modulator in each storage unit [2]. - The architecture employs FeFETs for multiplication operations, which exhibit low static power consumption and excellent linear characteristics in the subthreshold region [2]. - FeFETs demonstrate a sub-pA cutoff current and are expected to maintain performance over 10 years with over 10 million cycles [2]. Group 2: Optoelectronic Conversion and Lossless Summation - The architecture utilizes lithium niobate (LN) modulators for converting electrical signals to optical signals, leveraging the Pockels effect to achieve phase shifts in light signals [4][6]. - The integration of multiple 2T1M units in a Mach-Zehnder interferometer allows for effective accumulation of phase shifts, enabling lossless summation of vector-matrix multiplication results [4][6]. Group 3: Transformer Application - Experimental results indicate that the 2T1M architecture achieves a 93.3% inference accuracy when running the ALBERT model, significantly outperforming traditional CIM architectures, which only achieve 48.3% accuracy under the same conditions [9]. - The 2T1M architecture supports an array size of up to 3750kb, which is over 150 times larger than traditional CIM architectures limited to 256kb due to IR drop constraints [9]. - The architecture's power efficiency is reported to be 164 TOPS/W, representing a 37-fold improvement over state-of-the-art traditional CIM architectures, which is crucial for enhancing energy efficiency in edge computing and data centers [9].