Fig. 1: Software-hardware co-designed edge-pruning topology optimization.

Comparison between the proposed resistive-memory-based software-hardware co-design and conventional GPU-based weight optimization across biological, algorithmic, architectural, and circuit domains. Biologically, the approach is motivated by developmental pruning in the human brain, where repeated experiences eliminate redundant synapses and preserve critical connections, in contrast to conventional schemes grounded in long-term synaptic plasticity. At the algorithm level, edge-pruning topology optimization adjusts the connectivity of a randomly weighted, overparameterized dense network to form a sparse functional sub-network, distinct from methods relying on precise weight tuning. Architecturally, a hybrid analog-digital system with an analog compute core reduces data movement between memory and processor, addressing the von Neumann bottleneck associated with conventional digital architectures. At the circuit level, resistive memory supports parallel analog matrix multiplication and physically realizes edge pruning. Intrinsic programming stochasticity generates dense random weights (illustrated by the differential-conductance heatmap and histogram), while pruning and reinstatement are implemented by resetting or setting selected differential cell pairs to low- or high-conductance states.