Presentation
BiNeuroRAM: Energy-Efficient ReRAM-Based PIM for Accurate Bipolar Spiking Neural Network Acceleration
DescriptionReRAM is a promising non-volatile memory for neuromorphic accelerators, but challenges like high sensing power and accuracy loss persist. BiNeuroRAM, a novel SNN accelerator with ReRAM processing-in-memory (PIM), makes 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 prior state-of-the-art. (2) A low-power voltage sense amplifier (LPVSA) reduces ReRAM read power by 14.7 - 58.2×, addressing energy efficiency. (3) The asynchronous micro-architecture in BiNeuroRAM fully leverages the event-driven nature of SNNs. Our experiments demonstrate that BiNeuroRAM improves throughput density and energy efficiency by 2.1× on ImageNet with ResNet-18, compared to traditional integrate-and-fire (IF) neuron-based SNN accelerators.
Event Type
Research Manuscript
TimeTuesday, June 2410:30am - 10:45am PDT
Location3002, Level 3
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