X-PIM: Fast Modeling and Validation Framework for Mixed-Signal Processing-in-Memory Using Compressed Equivalent Model in System Verilog

Mixed-signal processing-in-memory (PIM) has gained prominence as a promising approach for implementing deep neural networks in energy-constrained systems. However, the co-design and optimization of mixed-signal circuits in PIM demand substantial time and effort for simulation and validation. This work presents X-PIM, a fast modeling and validation framework for mixed-signal PIMs. X-PIM encompasses not only the precise modeling of transistor-level analog computation circuits in System Verilog but also the rapid validation of system-level neural networks implemented using the mixed-signal PIM model. For achieving both accuracy and speed in simulation, X-PIM introduces a technique called compressed equivalent model (CEM) for the mixed-signal PIM circuits. This technique transforms a two-dimensional PIM array into an equivalent single-cell model. Furthermore, X-PIM can account for the impact of non-ideal operations in mixed-signal circuits by incorporating effects such as ADC quantization noise, parasitic components, intrinsic noise, and finite bandwidth. Based on the proposed mixed-signal PIM modeling, X-PIM can perform the system-level neural network validation with a significantly reduced simulation time at least 200 times faster than that of SPICE-based validation. X-PIM demonstrates three mixed-signal PIMs: XNOR PIM, capacitive PIM, and ReRAM-based PIM. For multi-layer perceptron (MLP) network, X-PIM can complete accuracy evaluation for MNIST-IOOO dataset within only 30 minutes.