Analog In-Memory Computing & ReRAM Crossbars
Enterprise Autonomous Agent Analog Neuromorphic Crossbar Memristor Compute Simulator
Benchmark analog in-memory computing (AIMC) using Resistive RAM (ReRAM) and Phase-Change Memory (PCM) crossbar arrays. Simulate single-step Kirchhoff/Ohm Vector-Matrix Multiplication (VMM), power-law conductance drift, and femtojoule-per-synaptic-op energy scaling against digital GPU baselines.
Analog Neuromorphic In-Memory Computing Performance Matrix
- Single-Step VMM Accumulation: Analog currents sum across bitlines in a single cycle via Kirchhoff's Current Law, completing a 256x256 matrix multiply in sub-3.5ns without memory bus transfers.
- Energy Efficiency: Consumes <65 fJ per synaptic operation, delivering over 35x lower power dissipation compared to 4nm digital GPU tensor cores.
- Conductance Drift Resilience: Power-law relaxation dynamics are compensated via drift-resilient neural weight mapping and scheduled closed-loop write-verify refresh routines.