Polychronous Wave Computing: Timing-Native Address Selection in Spiking Networks
Not on the map. Why?
Abstract (arXiv)
Spike timing offers a combinatorial address space, suggesting that timing-based spiking inference can be executed as lookup and routing rather than as dense multiply--accumulate. Yet most neuromorphic and photonic systems still digitize events into timestamps, bins, or rates and then perform selection in clocked logic. We introduce Polychronous Wave Computing (PWC), a timing-native address-selection primitive that maps relative spike latencies directly to a discrete output route in the wave domain. Spike times are phase-encoded in a rotating frame and processed by a programmable multiport interferometer that evaluates K template correlations in parallel; a driven--dissipative winner-take-all stage then performs a physical argmax, emitting a one-hot output port. We derive the operating envelope imposed by phase wrapping and mutual coherence, and collapse timing jitter, static phase mismatch, and dephasing into a single effective phase-noise budget whose induced winner--runner-up margin predicts boundary-first failures and provides an intensity-only calibration target. Simulations show that nonlinear competition improves routing fidelity compared with noisy linear intensity readout, and that hardware-in-the-loop phase tuning rescues a temporal-order gate from 55.9% to 97.2% accuracy under strong static mismatch. PWC provides a fast routing coprocessor for LUT-style spiking networks and sparse top-1 gates (e.g., mixture-of-experts routing) across polaritonic, photonic, and oscillator platforms.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 2 of its 8 references that are in the map. First posted on arXiv 19 Jan 2026.
Builds on
2 of 8 references.
References · 2
- 2017Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layercited by 3,509
- 2025Spiking Manifestocited by 4
Built on by
Not cited yet.
Similar, not cited
Close in wording, no citation either way
- 2026Determinism in the Undetermined: Deterministic Output in Charge-Conserving Continuous-Time Neuromorphic Systems with Temporal Stochasticity
- 2026Otters++: A Time-to-first-spike Based Energy Efficient Optical Spiking Transformer
- 2025Otters: An Energy-Efficient SpikingTransformer via Optical Time-to-First-Spike Encoding
- 2024SpiDR: A Reconfigurable Digital Compute-in-Memory Spiking Neural Network Accelerator for Event-based Perception
- 2025Hardware-Software Collaborative Computing of Photonic Spiking Reinforcement Learning for Robotic Continuous Control
- 2025Predictive Spike Timing Enables Distributed Shortest Path Computation in Spiking Neural Networks
- 2025Delays in Spiking Neural Networks: A State Space Model Approach
- 2024A Fully-Configurable Open-Source Software-Defined Digital Quantized Spiking Neural Core Architecture