SACA: System-level Analog CIM Accelerators Simulation Framework: Accurate Simulation of Non-Ideal Components

Abstract

Always-ON accelerators running TinyML applications are strongly limited by the memory and computation resources available in edge devices. Compute-In-Memory (CIM) architectures based on non-volatile memories (NVM) promise to bring the required compute and memory demands of Deep Neural Networks (DNN) to the edge while consuming extremely low power. However, their system-level design is constrained by the device and periphery noise which strongly impacts and compromises the accuracy of the DNN workload. In this paper SACA, a framework for simulating host & CIM accelerator systems, is presented. The simulator quantifies the system reliability by taking into account device-level non-idealities. The accuracy of two representative TinyML workloads is analyzed based on the crossbar characteristics -NVM technology, crossbar size, periphery characteristics. To demonstrate the capabilities of SACA, extensive experiments are carried out. We have characterized a convolutional network tackling CIFAR10 image classification and a fully connected network performing Human Activity Recognition. The results lead to optimal energy/performance/accuracy profiles, while the overall analysis highlights the dramatic effects of IR-drop on larger crossbars, degrading the system's accuracy and compromising its reliability.