NeuAFG: Neural Network-Based Analog Function Generator for Inference in CIM

Resistive Random-Access Memory (RRAM)-based Compute-in-Memory (CIM) architectures offer promising solutions for energy-efficient deep neural network (DNN) inference. However, conventional CIM accelerators suffer from high energy consumption due to frequent analog-to-digital (AD) and digital-to-analog (DA) signal conversions, especially when computing nonlinear activation functions (NAFs). This paper presents NeuAFG, a time-domain analog function generator designed to directly compute arbitrary NAFs in the analog domain. NeuAFG utilizes a one-hidden-layer ReLU neural network (ReLUNet) to approximate a variety of activation functions, incorporating two novel optimization algorithms: Low-Discrepancy Search (LDSearch) for efficient parameter initialization and KDE-FocusSampler for focused sampling of regions that are difficult to approximate. The hardware implementation integrates RRAM-based components and employs a Pulse-In-Pulse-Out method for robust time-domain computation. Experimental results demonstrate that NeuAFG reduces energy consumption in mainstream CIM accelerators such as ISAAC and RAELLA by <tex-math notation="LaTeX">1.03× </tex-math> to <tex-math notation="LaTeX">2.20× </tex-math>, with less than 1% accuracy loss in DNN inference tasks.