Latency-Constrained DNN Architecture Learning for Edge Systems using Zerorized Batch Normalization
arXiv:2607.06922v1 Announce Type: new Abstract: Deep learning applications have been widely adopted on edge devices, to mitigate the privacy and latency issues of accessing cloud servers. Deciding the number of neurons during the design of a deep neural network to maximize performance is not intuitive. Particularly, many application scenarios are real-time and have a strict latency constraint, while conventional neural network optimization methods do not directly change the temporal cost of model inference for latency-critical edge systems. In this work, we propose a latency-oriented neural ne
The proliferation of AI at the edge and the increasing demand for real-time processing necessitates new architectural optimizations that directly address latency constraints.
This work directly tackles a critical bottleneck for real-time AI applications on edge devices, enhancing their practical viability and expanding deployment scenarios beyond cloud dependence.
The ability to design deep neural networks with built-in latency constraints will accelerate the deployment of performant AI on resource-limited edge hardware.
- · Edge AI device manufacturers
- · Real-time embedded systems developers
- · AI hardware accelerators
- · Generic cloud AI services for latency-critical applications
- · Developers unable to optimize for edge constraints
Improved performance and broader adoption of AI on edge devices across various industries.
Reduced reliance on centralized cloud infrastructure for certain AI workloads, enhancing data privacy and operational resilience.
Potential for new edge-native AI applications and business models where latency is paramount.
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Read at arXiv cs.LG