Generalized Latency Performance Estimation for Once-For-All Neural Architecture Search

TL;DR

This paper develops generalized latency prediction models for neural architecture search, enabling efficient and hardware-agnostic performance estimation that surpasses traditional lookup table methods in accuracy and flexibility.

Contribution

It introduces two strategies for building hardware-generalizable latency predictors, reducing manual effort and improving prediction accuracy in NAS.

Findings

Latency predictors achieve over 50% lower RMSE than ProxylessNAS.

Predictions match or exceed lookup table baseline performance.

Framework supports heterogeneous hardware deployment.

Abstract

Neural Architecture Search (NAS) has enabled the possibility of automated machine learning by streamlining the manual development of deep neural network architectures defining a search space, search strategy, and performance estimation strategy. To solve the need for multi-platform deployment of Convolutional Neural Network (CNN) models, Once-For-All (OFA) proposed to decouple Training and Search to deliver a one-shot model of sub-networks that are constrained to various accuracy-latency tradeoffs. We find that the performance estimation strategy for OFA's search severely lacks generalizability of different hardware deployment platforms due to single hardware latency lookup tables that require significant amount of time and manual effort to build beforehand. In this work, we demonstrate the framework for building latency predictors for neural network architectures to address the need for heterogeneous hardware support and reduce the overhead of lookup tables altogether. We introduce two generalizability strategies which include fine-tuning using a base model trained on a specific hardware and NAS search space, and GPU-generalization which trains a model on GPU hardware parameters such as Number of Cores, RAM Size, and Memory Bandwidth. With this, we provide a family of latency prediction models that achieve over 50% lower RMSE loss as compared to with ProxylessNAS. We also show that the use of these latency predictors match the NAS performance of the lookup table baseline approach if not exceeding it in certain cases.