CIM-FLEX: An Integer-Only Flexible Periphery for Distributed Compute-In-Memory Architectures

Distributed compute-in-memory (CIM) architectures are emerging as a path towards energy-efficient and highly parallel edge-class deep neural network (DNN) inference. However, the lack of a flexible, unified digital periphery limits the ability of distributed CIM systems to support integer-quantized DNN inference schemes, activation functions, and scalable tile-to-tile dataflow. We present CIM-FLEX, an integer-only periphery architecture that enables each CIM tile to autonomously perform partial-sum rescaling, asymmetric activation processing, and lightweight programmable activation functions. CIM-FLEX also supports mixed-precision integer multiply-and-accumulate (MAC) by decomposing higher-precision operations into uniform-precision partial MACs, without modifying existing CIM primitives. CIM-FLEX enables flexible tile-to-tile dataflow across distributed CIM tiles, exposing a tradeoff between parallel output generation and energy efficiency, where deeper accumulation chains reduce concurrent periphery utilization and improve overall efficiency. Numerical studies and TFLite-based LUT activation experiments validate a high signal-to-noise ratio (∼30 dB) for programmable activation functions, and a 14 nm CMOS implementation shows a 0.012 mm 2 area for the CIM-FLEX.