Quantisation-Aware Training for Edge Deployment: A Benchmark Study Across Three Accelerator Families
DOI:
https://doi.org/10.5281/zenodo.22657287Keywords:
Quantisation-Aware Training for Edge Deployment; Applied machine learning; Evidence synthesis; Production deployment; MLOpsAbstract
Quantisation-Aware Training for Edge Deployment is an area of active development in applied machine learning, with evidence
that has accumulated unevenly across its component questions. We review the current evidence base for quantisation-aware
training for edge deployment, spanning methodological, implementation, governance, and cost dimensions, focusing on where
practice has converged and where it continues to vary, and set out the practical considerations relevant to a team considering its
adoption. We conclude that selective, context-specific use is best supported by the evidence currently available, rather than
uniform adoption or avoidance.
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