Transformers have emerged as a powerful tool for natural language processing (NLP) and computer vision. Through the attention mechanism, these models have exhibited remarkable performance gains when compared to conventional approaches like recurrent neural networks (RNNs) and convolutional neural networks (CNNs). Nevertheless, transformers typically demand substantial execution time due to their extensive computations and large memory footprint. Processing in-memory (PIM) and near-memory computing (NMC) are promising solutions to accelerating transformers as they offer high compute parallelism and memory bandwidth. However, designing PIM/NMC architectures to support the complex operations and massive amounts of data that need to be moved between layers in transformer neural networks remains a challenge. We propose ARTEMIS, a mixed analog-stochastic in-DRAM accelerator for transformer models. Through employing minimal changes to the conventional DRAM arrays, ARTEMIS efficiently alleviates the costs associated with transformer model execution by supporting stochastic computing for multiplications and temporal analog accumulations using a novel in-DRAM metal-on-metal capacitor. Our analysis indicates that ARTEMIS exhibits at least 3.0x speedup, 1.8x lower energy, and 1.9x better energy efficiency compared to GPU, TPU, CPU, and state-of-the-art PIM transformer hardware accelerators.

通过使用 PIM/NMC 架构以及利用新型的 in-DRAM 金属-金属电容器支持随机计算和时间模拟累加，我们提出了 ARTEMIS，一个用于 Transformer 模型的混合模拟-随机的 in-DRAM 加速器，通过对传统 DRAM 数组进行最小化改变，ARTEMIS 在执行 Transformer 模型时高效地减轻了相关成本，相较于 GPU、TPU、CPU 和先进的 PIM Transformer 硬件加速器，ARTEMIS 达到了至少 3.0 倍的加速、1.8 倍低能量消耗以及 1.9 倍更好的能效。

ARTEMIS：Transformer神经网络的混合模拟-随机In-DRAM加速器