Ternary Neural Network

Ternary neural networks (TNNs) represent a significant advancement in energy-efficient deep learning by quantizing network weights to only three values (-1, 0, 1), drastically reducing computational and memory demands compared to full-precision networks. Current research focuses on improving TNN accuracy through novel training strategies like twin network augmentation and optimized quantization techniques such as support and mass equalization, as well as developing specialized hardware accelerators and instruction set extensions for efficient inference on resource-constrained devices like mobile CPUs and edge systems. This pursuit of efficient, low-power deep learning has broad implications for deploying AI in various applications, particularly those with limited power budgets or computational resources.