Scaling Laws of Patchout Audio Transformers on Edge Devices: Latency-Throughput Tradeoffs

Abstract

Abstract: This report synthesises findings from 7 peer-reviewed papers addressing the following research question: How does the inference efficiency of Patchout Audio Transformers scale with model size when deployed on edge devices, as measured by latency-throughput tradeoffs on the AVE-AV benchmark. The deployment of transformer-based models on resource-constrained edge devices represents a critical challenge in enabling real-time artificial intelligence applications. This comprehensive survey examines lightweight transformer architectures specifically designed for edge. 0 claims were extracted from source literature; 0 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 2.2/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

How does the inference efficiency of Patchout Audio Transformers scale with model size when deployed on edge devices, as measured by latency-throughput tradeoffs on the AVE-AV benchmark?

Verification Level

Descriptive public verification status only; aggregate claim counts are public, but individual claim records are not exposed here.

Quality Tier

Descriptive public triage only; this tier does not alter current publication or DOI behavior.

Quality Dimensions

Automated triage signals derived from public fields; not human peer review or independent validation.

Correction Record

Public corrections are additive records. Current status does not claim the synthesis is error-free.

Provenance