analog-interaction-systems-ais

name	analog-interaction-systems-ais
description	Analog Interaction Systems (AIS) methodology for energy-efficient generative modeling on neuromorphic hardware. Bridges gap between software-defined generative models and fixed physics-determined differential equations in analog circuits. Use when designing low-power generative models, implementing analog/neuromorphic computing for ML, optimizing energy-efficient AI hardware, or working with oscillator-based dynamical systems.
metadata	{"arxiv_id":"2606.27294","published":"2026-06-25","authors":"Yu-Neng Wang, Sara Achour","categories":"cs.ET, cs.LG","tags":["analog computing","neuromorphic","generative models","energy efficiency","dynamical systems","oscillators"]}

Analog Interaction Systems (AIS)

AIS is a unified framework for hardware-implementable dynamical systems that enables generative modeling on analog hardware with 100x energy savings over digital baselines.

Core Problem

Modern generative models assume flexible, software-defined dynamics, but analog hardware imposes fixed, physics-determined differential equations with limited approximation capacity. This creates an expressivity gap.

Methodology

1. Framework Definition

Define AIS as dynamical systems implementable on analog hardware:

• Coupled oscillators (phase dynamics)
• Analog Ising Machines (spin dynamics)
• Other physics-determined differential equation solvers

2. Expressivity Gap Characterization

Measure performance gap relative to neural network baselines:

• Evaluate on standard generative tasks (MNIST, Fashion-MNIST)
• Compare FID scores against digital equivalents
• Quantify approximation capacity limitations

3. Gap-Narrowing Mechanisms

Implement two hardware-compatible mechanisms:

Mechanism A: Time-varying piecewise parameters

• Divide generation into temporal segments
• Switch parameters at segment boundaries
• Approximates time-varying dynamics within fixed hardware constraints

Mechanism B: Hidden physical states

• Introduce auxiliary state variables
• Expand effective dimensionality without additional hardware
• Leverage physical degrees of freedom already present in analog circuits

4. Training Procedure

Use Wasserstein GAN (WGAN) training:

• Does not require trajectory following (unlike ODE-based training)
• Optimizes for distribution matching rather than trajectory matching
• Compatible with fixed-point attractor dynamics of analog hardware

5. Hardware Implementation Analysis

Characterize scaling with:

• Connection density: Sparse connectivity required (fewer connections = lower power)
• Precision: Low-bit-width quantized parameters (4-bit shown sufficient)
• Energy: Measure joules per generated sample

Key Results

• MNIST: FID 27.6 with 4-bit sparse architecture
• Fashion-MNIST: FID 80.8 with 4-bit sparse architecture
• Energy: 23 μJ per generated image
• Improvement: 3-4x better FID than prior analog generative models
• Energy savings: ~100x vs digital baselines (2 orders of magnitude)

Design Constraints

Sparse connectivity is necessary: Dense connections increase power beyond practical limits for edge deployment.

Low precision is sufficient: 4-bit quantization maintains quality while enabling practical implementation.

Physics-first design: Choose dynamics that match hardware physics rather than approximating arbitrary software dynamics.

Pitfalls

Expressivity gap is fundamental: Cannot eliminate entirely, only narrow through architectural innovations. Don't expect analog systems to match digital flexibility.

Trajectory-based training fails: Analog systems converge to attractors, not trajectories. WGAN-style distribution matching is required.

Precision-performance tradeoff: Below 4-bit, FID degrades rapidly. 4-bit appears to be practical minimum for useful generation.

Hardware-software co-design required: Cannot simply port digital architectures. Must redesign from physics constraints upward.

Applications

• Edge AI with strict power budgets
• IoT generative models
• Neuromorphic computing platforms
• Physics-informed generative design
• Low-power image/signal generation

Activation Keywords

analog computing, neuromorphic, AIS, analog interaction systems, coupled oscillators, analog ising machine, energy-efficient generative, hardware-implementable dynamics, physics-determined differential equations, low-power AI, edge generative models, dynamical systems hardware