AI RESEARCH
E-ReCON: An Energy- and Resource-Efficient Precision-Configurable Sparse nvCIM Macro for Conventional and Spiking Neural Edge Inference
arXiv CS.CV
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ArXi:2605.20717v1 Announce Type: cross This work presents E-ReCON, a 16 Kb energy and resource-efficient digital compute-in-memory (DCIM) macro based on a compact 3T1R ReRAM bitcell for edge-AI inference. The proposed bitcell occupies only 0.85 um^2 and s reliable AND-based in-memory multiplication for both conventional convolutional neural network (CNN) and spiking neural network (SNN) workloads. To reduce accumulation overhead, a novel interleaved 10T/28T adder tree is