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Computing-in-memory (CiM) is a promising technique to achieve high energyefficiency in data-intensive matrix-vector multiplication (MVM) by relievingthe memory bottleneck. Unfortunately, due to the limited SRAM capacity,existing SRAM-based CiM needs to reload the weights from DRAM in large-scalenetworks. This undesired fact weakens the energy efficiency significantly. Thiswork, for the first time, proposes the concept, design, and optimization ofcomputing-in-ROM to achieve much higher on-chip memory capacity, and thus lessDRAM access and lower energy consumption. Furthermore, to support differentcomputing scenarios with varying weights, a weight fine-tune technique, namelyResidual Branch (ReBranch), is also proposed. ReBranch combines ROM-CiM andassisting SRAM-CiM to ahieve high versatility. YOLoC, a ReBranch-assistedROM-CiM framework for object detection is presented and evaluated. With thesame area in 28nm CMOS, YOLoC for several datasets has shown significant energyefficiency 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.

YOLoC: DeploY Large-Scale Neural Network by ROM-based Computing-in-Memory using ResiduaL Branch on a Chip