arXiv:2607.04531v1 Announce Type: cross Abstract: Low-precision neural networks are attractive for resource-constrained hardware, but fixed-point arithmetic introduces failure modes that are often hidden by idealised quantisation models. In particular, two's-complement overflow wrapping can corrupt hidden activations by changing both their magnitude and sign, leading to unstable numerical error propagation and severe accuracy degradation. This paper proposes a Lyapunov-stabilised quantisation framework for low-precision neural networks operating under hardware-style wrapping arithmetic. The hi
The increasing push for deploying AI in resource-constrained edge devices and the prevalence of low-precision arithmetic in such hardware necessitates robust solutions for numerical stability.
Ensuring hardware-safe neural networks at low precision is critical for the reliable and widespread adoption of AI in diverse, real-world applications, especially in embedded systems.
This research offers a method to mitigate critical failure modes in low-precision AI hardware, potentially accelerating the development and deployment of more resilient and efficient AI systems.
- · Edge AI hardware manufacturers
- · Embedded AI developers
- · Hardware-constrained industries
- · High-reliability AI applications
- · Developers relying on idealized quantization models
- · AI projects with insufficient hardware-aware design
Improved reliability and power efficiency for AI deployed on edge devices.
Faster integration of complex AI models into consumer electronics and industrial control systems due to reduced risk of numerical errors.
Enhanced trust and broader market acceptance of AI in safety-critical applications, previously hindered by hardware instability concerns.
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Read at arXiv cs.AI