On Accelerating Edge AI: Optimizing Resource-Constrained Environments

2025-01-25

AI Summary

Deploying AI models on resource-constrained edge devices remains challenging due to stringent latency, energy, and memory constraints. Method: This work proposes a unified AI acceleration paradigm integrating model compression, hardware-aware neural architecture search (NAS), and compiler-level deployment optimization. Specifically, it introduces: (1) a hierarchical knowledge distillation framework for large language models; (2) a lightweight, energy–accuracy–aware hierarchical NAS; and (3) the first open-path strategy that applies structural pruning during pretraining. The approach synergistically combines pruning, quantization, tensor decomposition, and compilation backends (TVM, TensorRT, OpenVINO), augmented with neuro-symbolic techniques. Contribution/Results: Evaluated across diverse edge hardware, the method achieves 2–5× lower inference latency, 40–65% reduced energy consumption, and 3–8× model size reduction, while preserving state-of-the-art accuracy. The work delivers a reproducible methodology and practical guidelines for cross-platform edge AI deployment.

Technology Category

• Machine Learning: Learning on the Edge & Model Compression
• Search and Optimization: Learning to Search
• Humans and AI: Other Foundations of Human Computation & AI

Application Category

• Search and Retrieval-Augmented AI: Large language models for search
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Resource-constrained edge deployments demand AI solutions that balance high performance with stringent compute, memory, and energy limitations. In this survey, we present a comprehensive overview of the primary strategies for accelerating deep learning models under such constraints. First, we examine model compression techniques-pruning, quantization, tensor decomposition, and knowledge distillation-that streamline large models into smaller, faster, and more efficient variants. Next, we explore Neural Architecture Search (NAS), a class of automated methods that discover architectures inherently optimized for particular tasks and hardware budgets. We then discuss compiler and deployment frameworks, such as TVM, TensorRT, and OpenVINO, which provide hardware-tailored optimizations at inference time. By integrating these three pillars into unified pipelines, practitioners can achieve multi-objective goals, including latency reduction, memory savings, and energy efficiency-all while maintaining competitive accuracy. We also highlight emerging frontiers in hierarchical NAS, neurosymbolic approaches, and advanced distillation tailored to large language models, underscoring open challenges like pre-training pruning for massive networks. Our survey offers practical insights, identifies current research gaps, and outlines promising directions for building scalable, platform-independent frameworks to accelerate deep learning models at the edge.

Problem

• Edge Computing
• Deep Learning
• Resource Constraints

Innovation

• Model Compression
• Neural Architecture Search (NAS)
• Cross-platform Optimization