Noise-shaped Sampling and Annealing for Scalable On-the-edge Training of Binary and Ternary AI Models

Key points

• We exploit this equivalence to apply Ising solvers to discrete-weight network training, while accounting for a fundamental difference in objectives: an Ising solver seeks the ground state of a fixed energy function, whereas training seeks solutions that generalize to unseen data.1
• Our key contribution is $ΣΔ$-Sampler, an optimizer that reconciles these objectives by embedding a stochastic sampling-and-annealing step as a noisy quantizer inside a first-order $ΣΔ$ noise-shaping loop.1
• The loop pushes noise from weight quantization, high-order frustration, and mini-batch sampling out of the low-frequency adaptation band, so that discrete weight updates follow the smooth descent trajectories embedded in the rugged loss landscape.1
• Compared with standard quantization-aware training, $ΣΔ$-Sampler reduces the training memory footprint by over $50%$, delivers an order-of-magnitude speed-up, and reduces weight-update memory traffic by four orders of magnitude, making it attractive for on-device training and fine-tuning of large binary and ternary models.1

Sources (1)

• [1]Noise-shaped Sampling and Annealing for Scalable On-the-edge Training of Binary and Ternary AI Models
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 15, 08:23 PM

  The training loss of binary and ternary neural networks, expanded as a multilinear polynomial over the discrete weights, is structurally equivalent to the energy of a hierarchical high-order Ising model.

  We exploit this equivalence to apply Ising solvers to discrete-weight network training, while accounting for a fundamental difference in objectives: an Ising solver seeks the ground state of a fixed energy function, whereas training seeks solutions that generalize to unseen data.

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