Abstract: Compute-in-memory (CIM) is a promising approach for realizing energy-efficient deep neural network (DNN) accelerators. Previous CIM works focusing on uniform quantization (UQ) demonstrated a ...

Abstract: Device sizing is crucial for meeting performance specifications in operational transconductance amplifiers (OTAs), and this work proposes an automated sizing framework based on a transformer ...