Computing-in-memory (CIM) is a promising architecture for energy-efficient neural network (NN) processors. Several CIM macros have demonstrated high energy efficiency, while CIM-based system-on-a-chip is not well explored. This work presents a CIM NN processor, named STICKER-IM, which is implemented with sophisticated system integration. Three key innovations are proposed. First, a CIM-friendly block-wise sparsity (BWS) architecture is designed, enabling both activation-sparsity-aware acceleration and weight-sparsity-aware power-saving. Second, an adaptive kernel-/channel-order (KCO) mapping and intra-/inter-macro scheduling strategy is proposed to improve macro utilization and data reuse. Third, an efficient BWS-optimized CIM (BWS-CIM) macro with adaptive power-OFF ADCs is implemented. The STICKER-IM chip was fabricated in 65-nm CMOS technology. Experimental results show 5.8–158-TOPS/W average system energy efficiency on the sparse NN models. The macro/system-level energy efficiency is $4.23\times / 3.06\times$ higher compared with the state-of-the-art CIM macros and processors.