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DTSTAMP:20260402T024533Z
LOCATION:3002\, Level 3
DTSTART;TZID=America/Los_Angeles:20250624T103000
DTEND;TZID=America/Los_Angeles:20250624T104500
UID:dac_DAC 2025_sess162_RESEARCH1631@linklings.com
SUMMARY:BiNeuroRAM: Energy-Efficient ReRAM-Based PIM for Accurate Bipolar 
 Spiking Neural Network Acceleration
DESCRIPTION:Jun Yan Lee, Chen Nie, kang you, Yueyang Jia, Rui Yang, and Zh
 ezhi He (Shanghai Jiao Tong University)\n\nReRAM is a promising non-volati
 le 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 t
 han 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 i
 mproves throughput density and energy efficiency by 2.1× on ImageNet with 
 ResNet-18, compared to traditional integrate-and-fire (IF) neuron-based SN
 N accelerators.\n\nTopics: Design\n\nTracks: DES2B: In-memory and Near-mem
 ory Computing Architectures, Applications and Systems\n\nSession Chairs: X
 ueqing Li (Tsinghua University) and Wantong Li (University of California, 
 Irvine)\n\n
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