Neural Network Quantization for Microcontrollers: A Comprehensive Survey of Methods, Platforms, and Applications

Abstract

This comprehensive survey addresses the challenges of deploying Quantized Neural Networks (QNNs) on resource-constrained microcontrollers (MCUs) within the TinyML paradigm. It systematically reviews hardware-oriented quantization methods, focusing on the critical trade-offs between model performance and MCU hardware capabilities, including memory constraints and numerical precision. The paper further analyzes contemporary hardware platforms, spanning ARM-based and RISC-V-based designs with integrated Neural Processing Units (NPUs), alongside supporting software stacks and real-world applications.

Report

Key Highlights

• Hardware-Oriented Focus: The survey provides a systematic, hardware-centric review of neural network quantization methods optimized specifically for deployment on resource-constrained microcontrollers (MCUs) and extreme-edge devices.
• Critical Trade-offs: Emphasis is placed on managing the essential trade-offs required for TinyML: balancing model performance (accuracy) against strict computational complexity and memory constraints of MCU-class hardware.
• Platform Coverage: The analysis comprehensively reviews contemporary MCU hardware platforms, explicitly including both ARM-based and RISC-V-based architectures, and devices that integrate Neural Processing Units (NPUs).
• Full Ecosystem View: The paper reviews not only algorithms (quantization methods) but also the necessary supporting software stacks and consolidates real-world application domains where these quantized systems are successfully deployed.

Technical Details

• Target Domain: Tiny Machine Learning (TinyML) inference on extreme-edge devices.
• Core Technique: Neural Network Quantization (QNNs), reducing precision (e.g., from 32-bit floating point to 8-bit integer) to save memory and compute.
• Hardware Constraints: Evaluation criteria focus on memory hierarchies, low-precision numerical representations, and the presence of dedicated hardware accelerators (Neural Processing Units or NPUs).
• Architectures Covered: Survey includes review of implementation strategies across ARM-based and RISC-V-based MCU designs.
• Software Stacks: Analysis covers the supporting software environments necessary to bridge the gap between quantized models and low-resource hardware.

Implications

• RISC-V as a Core TinyML Platform: The inclusion of RISC-V-based designs in the comprehensive survey validates RISC-V as a primary, competitive Instruction Set Architecture (ISA) for future TinyML and edge AI deployments.
• Accelerator Demand: The necessity for NPUs in MCUs strongly suggests that specialized RISC-V extensions or custom instructions designed for highly efficient, low-precision fixed-point arithmetic will be crucial for competitive performance in the AI acceleration space.
• Ecosystem Guidance: The findings guide RISC-V developers and hardware designers in optimizing memory architectures and providing native support for the numerical representations most utilized by QNNs to achieve energy-efficient and scalable AI deployment.
• Software Compatibility: Highlights the need for robust, optimized software frameworks and compilers within the RISC-V ecosystem to seamlessly implement and deploy the surveyed quantization techniques.