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Memristor-based spiking neural network accelerator for bio-inspired interception tasks - achieving 12.7x energy reduction vs digital SNN (arXiv:2605.31299v1, May 2026).
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| name | memristor-snn-interception-task |
| description | Memristor-based spiking neural network accelerator for bio-inspired interception tasks - achieving 12.7x energy reduction vs digital SNN (arXiv:2605.31299v1, May 2026). |
| version | 2.0.0 |
| category | neuromorphic |
| tags | ["spiking-neural-network","memristor","neuromorphic-hardware","analog-computation","energy-efficient","edge-intelligence","interception"] |
| arxiv_id | 2605.31299v1 |
| authors | ["Qianhou Qu","Sheng Lu","Liuting Shang","Jaihan Utailawon","Sungyong Jung","Qilian Liang","Chenyun Pan"] |
| published | 2026-05-29T00:00:00.000Z |
| conference | IEEE Dallas Circuits and Systems Conference (DCAS 2026) |
This paper presents an analog memristor-based spiking neural network (SNN) accelerator that integrates in-memory synaptic computation with analog integrate-and-fire neurons, achieving significant energy efficiency gains over digital implementations.
Key Achievement: 12.7x lower energy consumption and 1.26x lower latency compared to digital SNN baseline at 5nm technology node.
In-Memory Synaptic Computation
Analog Integrate-and-Fire (IF) Neurons
Event-Driven Operation
| Metric | Analog SNN (45nm) | Digital SNN (5nm) |
|---|---|---|
| Technology | 45nm | 5nm (advanced) |
| Energy per inference | 12.7x lower | Baseline |
| Latency | 1.26x lower | Baseline |
| Synapse implementation | Memristor arrays | CMOS circuits |
| Neuron type | Analog IF | Digital IF |
# Conceptual model of memristor synapse operation
class MemristorSynapse:
def __init__(self, resistance_range):
self.R_min = resistance_range[0] # Low resistance (strong connection)
self.R_max = resistance_range[1] # High resistance (weak connection)
def compute(self, input_voltage):
# Analog voltage → current through memristor
# Current = Voltage / Resistance
return input_voltage / self.resistance
def update_weight(self, conductance_change):
# Resistance modification (plasticity)
self.resistance -= conductance_change
Input spikes → Integration (charge accumulation) → Threshold check → Spike output
(analog integrator) (analog comparator) (pulse generator)
基于 SOC 职业分类
