Compute-In-Memory (CIM) represents a cutting-edge solution for achieving high- throughput and high energy efficiency for Deep Neural Networks (DNNs) acceleration. However, AI workloads require hardware accelerators that can handle more than just dense Vector-Matrix Multiplications (for which CIM memory-tiles are uniquely well-suited). A modest, but computationally crucial, number of auxiliary operations are often required by modern DNNs. Since these operations are often not suitable for CIM memory-tiles, some sort of digital compute-cores are required, preferably implemented with extremely high energy-efficiency. To meet this requirement, the Analog AI team has designed a heteroge- neous accelerator that merges analog CIM memory-tiles with custom digital compute-cores into an “analog fabric”.