AM-CIM: Approximate Memory Based Near Sensor Compute-in-Memory Architecture for Keyword Spotting

Compute-In-Memory (CIM) has emerged as a promising solution to address the von-Neumann bottleneck, making it a key technology for intelligent computing in edge IoT devices, particularly for real-time applications like keyword spotting (KWS). However, traditional CIM architectures face challenges such as high resource consumption, especially in data conversion, which can significantly impact chip area and energy efficiency. To address these challenges, this work proposes a computational CIM architecture utilizing multilevel analog memory, named AM-CIM, tailored for near-sensor (NS) computation of real-time KWS applications. Additionally, approximate memory technology is integrated into the AM-CIM architecture, employing data resilience scheduling for analog memory which contributes to significant reductions in hardware overhead. This integration facilitates a hardware-software co-design approach. To deploy KWS tasks in AM-CIM, a gated recurrent unit (GRU) network, referred to as MAC-GRU, is implemented. By employing Mel-energy as the input feature at the near-sensor end, the system achieves a 93.13% reduction in feature extraction power consumption. Evaluation results based on TSMC 180-nm technology demonstrate that the AM-CIM architecture achieves an accuracy of 88.51% for 10-keyword classification with a power consumption of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$546~\mu W$</tex-math> </inline-formula>, while reducing analog memory area by 43.32%.