基于XNOR比特计数的二进制神经网络高效推理加速器
An Optical XNOR-Bitcount Based Accelerator for Efficient Inference of Binary Neural Networks
摘要：
与全精度卷积神经网络（CNN）相比，二进制神经网络（BNN）越来越受到青睐，以减少推理处理的内存和计算需求，同时降低精度。BNN将CNN模型参数转换为1位精度，允许使用简单的XNOR和位计数操作处理BNN的推断。这使得BNN适合硬件加速。已经提出了几种基于光子集成电路（PIC）的BNN加速器。尽管这些加速器提供比它们的电子对应物显著更高的吞吐量和能量效率，但是这些加速器中所利用的XNOR和位计数电路需要进一步增强以提高它们的面积、能量效率和吞吐量。本文旨在满足这一需求。为此，我们发明了一种基于单MRR的光学XNOR门（OXG）。此外，我们提出了一种新的设计的位计数电路，我们称之为光电荷累加器（PCA）。采用密集波分复用（DWDM）级联多个OXG，并将其连接到PCA，构建了一种基于XNOR比特数的二进制神经网络加速器（OXBNN）。我们对四个现代BNN的推理进行的评估表明，OXBNN在每秒帧数（FPS）和FPS/W（能源效率）方面分别比先前工作中的两个基于PIC的BNN加速器的几何平均值提高了62倍和7.6倍。我们开发了一个事务级、事件驱动的基于Python的模拟器，用于评估加速器（https：//github.com/uky-UCAT/B_ONN_SIM）。
英文摘要：
Binary Neural Networks (BNNs) are increasingly preferred over full-precision Convolutional Neural Networks(CNNs) to reduce the memory and computational requirements of inference processing with minimal accuracy drop. BNNs convert CNN model parameters to 1-bit precision, allowing inference of BNNs to be processed with simple XNOR and bitcount operations. This makes BNNs amenable to hardware acceleration. Several photonic integrated circuits (PICs) based BNN accelerators have been proposed. Although these accelerators provide remarkably higher throughput and energy efficiency than their electronic counterparts, the utilized XNOR and bitcount circuits in these accelerators need to be further enhanced to improve their area, energy efficiency, and throughput. This paper aims to fulfill this need. For that, we invent a single-MRR-based optical XNOR gate (OXG). Moreover, we present a novel design of bitcount circuit which we refer to as Photo-Charge Accumulator (PCA). We employ multiple OXGs in a cascaded manner using dense wavelength division multiplexing (DWDM) and connect them to the PCA, to forge a novel Optical XNOR-Bitcount based Binary Neural Network Accelerator (OXBNN). Our evaluation for the inference of four modern BNNs indicates that OXBNN provides improvements of up to 62x and 7.6x in frames-per-second (FPS) and FPS/W (energy efficiency), respectively, on geometric mean over two PIC-based BNN accelerators from prior work. We developed a transaction-level, event-driven python-based simulator for evaluation of accelerators (https://github.com/uky-UCAT/B_ONN_SIM).
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