BiNeuroRAM: Energy-Efficient ReRAM-Based PIM for Accurate Bipolar Spiking Neural Network Acceleration

ReRAM is a promising non-volatile memory for neuromor-phic accelerators, yet it faces challenges such as high sensing power and accuracy degradation. This work proposes BiNeuroRAM, a novel spiking neural network (SNN) accelerator leveraging ReRAM-based processing-in-memory (PIM), with three key contributions: (1) It is the first to support higher-accuracy spike-tracing bipolar-integrate-and-fire (ST-BIF) neurons, achieving 80.9% accuracy on ImageNet, 8.4% higher than the previous state-of-the-art; (2) It introduces a low-power voltage sense amplifier (LPVSA) that reduces ReRAM read power by 14.7~58.2×, enhancing energy efficiency; (3) It employs an asynchronous micro-architecture that fully exploits the event-driven nature of SNNs. Experimental results show that BiNeuroRAM improves throughput density and energy efficiency by 2.08× and 2.09× on ImageNet with ResNet-18, compared to traditional integrate-and-fire (IF) neuron-based SNN accelerators.