论文标题
Yoloc：通过基于ROM的计算在芯片上使用残留分支来部署大规模神经网络
YOLoC: DeploY Large-Scale Neural Network by ROM-based Computing-in-Memory using ResiduaL Branch on a Chip
论文作者
论文摘要
内存计算（CIM）是一种有前途的技术，可以通过缓解存储器瓶颈来实现数据密集型矩阵矢量乘法（MVM）的高能量效率。不幸的是，由于SRAM容量有限，现有的基于SRAM的CIM需要在大规模网络中重新加载DRAM的重量。这个不希望的事实显着削弱了能源效率。这项工作首次提出了计算机计算的概念，设计和优化，以实现更高的芯片内存能力，从而减少了DRAM访问和较低的能源消耗。此外，还提出了一种体重微调技术，即残留分支（Rebranch），也提出了重量微调技术。 Rebranch结合了ROM-CIM，并协助SRAM-CIM来刺激高通用性。提出和评估了Yoloc是一个重组辅助的ROM-CIM框架，用于对象检测。在28nm CMO中相同的区域，几个数据集的Yoloc显示，Yolo的Yolo显着提高了14.8倍（DarkNet-19），RESNET-18倍4.8倍，与完全基于SRAM CIM相比，RESNET-18的Yolo（darknet-19）的潜伏期延迟<8％，几乎没有平均平均精度（MAP）损失（-0.5％〜 +0.2％）。
Computing-in-memory (CiM) is a promising technique to achieve high energy efficiency in data-intensive matrix-vector multiplication (MVM) by relieving the memory bottleneck. Unfortunately, due to the limited SRAM capacity, existing SRAM-based CiM needs to reload the weights from DRAM in large-scale networks. This undesired fact weakens the energy efficiency significantly. This work, for the first time, proposes the concept, design, and optimization of computing-in-ROM to achieve much higher on-chip memory capacity, and thus less DRAM access and lower energy consumption. Furthermore, to support different computing scenarios with varying weights, a weight fine-tune technique, namely Residual Branch (ReBranch), is also proposed. ReBranch combines ROM-CiM and assisting SRAM-CiM to ahieve high versatility. YOLoC, a ReBranch-assisted ROM-CiM framework for object detection is presented and evaluated. With the same area in 28nm CMOS, YOLoC for several datasets has shown significant energy efficiency improvement by 14.8x for YOLO (Darknet-19) and 4.8x for ResNet-18, with <8% latency overhead and almost no mean average precision (mAP) loss (-0.5% ~ +0.2%), compared with the fully SRAM-based CiM.
