A Fault-Tolerant Spike-Time Interface for Approximate Agreement in Distributed Neuromorphic Systems

2026-08-12

AI Summary

研究解决了分布式神经形态系统中处理单元间共享参数不一致的问题，通过引入一种基于尖峰时间的容错接口SIF及SpikeTrim算法来减少差异。

Abstract

Large neuromorphic systems contain many processing tiles that may replicate a shared control parameter such as a threshold reference. If these copies diverge, identical inputs may be processed under different intended settings. We study how tiles can reduce this disagreement when communication carries only labeled spike times and up to \(f\) sender labels may be Byzantine. A raw event stream cannot supply the one-value-per-sender input required by classical approximate agreement because a faulty sender can remain silent, flood a receiver, or report different times to different receivers. We introduce the Spike-time Interface for Faults, or \SIF, which combines paced epochs, sender attribution, per-label \FirstSpike admission, bounded timing error, and a silence sentinel. For an affine one-spike code, midpoint decoding attains the exact deterministic minimax error \(ρ=\min\{1/2,ω/L\}\), where \(ω\) is the residual timing uncertainty and \(L\) is the usable encoding window. \SpikeTrim applies the classical mean-subsequence-reduced (MSR) rule to the sender-indexed decoded values. For \(n\ge3f+1\), it guarantees one-step robust validity, the tight noiseless contraction factor \(f/(n-2f)\) under direct updates, an explicit worst-case asymptotic disagreement bound, and finite recovery after transient agreement-state corruption. A closed-form test determines whether a validated timing budget meets a target disagreement. Simulations illustrate the fault threshold, timing dependence, flooding resistance, and recovery. A controlled spiking classifier experiment shows an association between faster control-state alignment and lower prediction disagreement under a finite maintenance budget.

Problem

• Fault-Tolerant
• Spike-Time Interface
• Distributed Neuromorphic Systems
• Approximate Agreement

Innovation

• Spike-time Interface for Faults
• SIF
• Fault-Tolerance
• Approximate Agreement
• Distributed Neuromorphic Systems