Mitigating Refractory Blindness and Somatic Freezing in Dense Neuromorphic Vision Processors via an Asymmetric Input Funnel Architecture and NVIDIA GPU Co-Design

Second-order Current-Based (CUBA) Leaky Integrate-and-Fire (LIF) circuits form the foundation of low-power neuromorphic spiking hardware. However, when interfacing directly with high-temporal-resolution Dynamic Vision Sensors (DVS), unconstrained afferent event storms induce severe dynamical failure modes: integer bit-width wrapping, subtractive brake echo latchup, and subnegative somatic freezing. While Adaptive Threshold LIF (ALIF) attempts to regulate runaway somatic excitation, dynamic threshold expansion requires continuous memory read-modify-write bandwidth and wide comparator banks without shielding dendritic entry registers. This paper presents an Asymmetric Input Current Funnel Shunting Architecture combined with an NVIDIA PyTorch-native CUDA software co-design framework. The system confines all dynamic attenuation, saturation bounding, and reactive shunting to the channel entry gate (u1) while anchoring the somatic threshold comparator to a static transistor rail (Vth = –55.0 mV). The architecture integrates four operational mechanisms: (1) an edge-triggered Synchronous Zero-Flush Register, (2) a Gated Rectified Linear Current Sink with strict non-negative projection, (3) a Dimensionally Scaled Coupling Accumulator, and (4) a Soft-Bounded Bidirectional STDP Engine with homeostatic L1 conductance budgeting implemented via power-of-two barrel shifts. Validated through GPU-accelerated PyTorch tensor simulations and synthesizable two-stage pipelined Verilog across a comprehensive verification battery, the core eradicates the Sensory Blindness Duty Cycle (0.0% vs. 99.5% in unshielded baselines), guarantees zero arithmetic overflows under 100% storm saturation, bounds maximum firing to a 34% duty cycle, and achieves instant recovery from quiescent intervals. Gate-level logic synthesis reveals a low-overhead footprint of 35 flip-flops and 1,385 combinational gate primitives.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-10-09
DOI
https://doi.org/10.5281/zenodo.23250916
Primary Topic
Advanced Memory and Neural Computing
Type
article
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article

Mitigating Refractory Blindness and Somatic Freezing in Dense Neuromorphic Vision Processors via an Asymmetric Input Funnel Architecture and NVIDIA GPU Co-Design

Christopher Rigano
Zenodo (CERN European Organization for Nuclear Research)
Advanced Memory and Neural Computing
article

Mitigating Refractory Blindness and Somatic Freezing in Dense Neuromorphic Vision Processors via an Asymmetric Input Funnel Architecture and NVIDIA GPU Co-Design

Christopher Rigano
article en

Abstract

Second-order Current-Based (CUBA) Leaky Integrate-and-Fire (LIF) circuits form the foundation of low-power neuromorphic spiking hardware. However, when interfacing directly with high-temporal-resolution Dynamic Vision Sensors (DVS), unconstrained afferent event storms induce severe dynamical failure modes: integer bit-width wrapping, subtractive brake echo latchup, and subnegative somatic freezing. While Adaptive Threshold LIF (ALIF) attempts to regulate runaway somatic excitation, dynamic threshold expansion requires continuous memory read-modify-write bandwidth and wide comparator banks without shielding dendritic entry registers. This paper presents an Asymmetric Input Current Funnel Shunting Architecture combined with an NVIDIA PyTorch-native CUDA software co-design framework. The system confines all dynamic attenuation, saturation bounding, and reactive shunting to the channel entry gate (u1) while anchoring the somatic threshold comparator to a static transistor rail (Vth = –55.0 mV). The architecture integrates four operational mechanisms: (1) an edge-triggered Synchronous Zero-Flush Register, (2) a Gated Rectified Linear Current Sink with strict non-negative projection, (3) a Dimensionally Scaled Coupling Accumulator, and (4) a Soft-Bounded Bidirectional STDP Engine with homeostatic L1 conductance budgeting implemented via power-of-two barrel shifts. Validated through GPU-accelerated PyTorch tensor simulations and synthesizable two-stage pipelined Verilog across a comprehensive verification battery, the core eradicates the Sensory Blindness Duty Cycle (0.0% vs. 99.5% in unshielded baselines), guarantees zero arithmetic overflows under 100% storm saturation, bounds maximum firing to a 34% duty cycle, and achieves instant recovery from quiescent intervals. Gate-level logic synthesis reveals a low-overhead footprint of 35 flip-flops and 1,385 combinational gate primitives.

Zenodo (CERN European Organization for Nuclear Research)
Openalex Percentile: Top 23%
Advanced Memory and Neural Computing
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