Powering Down AI: How Layer-Wise Optimization Slashes Neural Network Energy by 58%
By Jiaxun Fang, Li Zhang, Shaoyi Huang
Published on November 24, 2025| Vol. 1, Issue No. 1
Content Source
This is a curated briefing. The original article was published on cs.LG updates on arXiv.org.
Summary
This research introduces a novel energy-aware, layer-wise compression framework designed to significantly reduce the power consumption of Convolutional Neural Networks (CNNs) running on systolic array accelerators. Addressing the energy dominance of Multiply-Accumulate (MAC) units, the proposed method builds a sophisticated layer-aware MAC energy model by combining per-layer activation statistics with partial sum transition analysis, integrated with a tile-level systolic mapping to accurately estimate convolution-layer energy. Based on this model, the framework employs an energy-accuracy co-optimized weight selection algorithm within quantization-aware training and an energy-prioritized layer-wise schedule that aggressively compresses high-energy layers while maintaining a global accuracy constraint. Experiments demonstrate an impressive energy reduction of up to 58.6% with only a 2-3% accuracy drop, surpassing existing power-aware baselines.
Why It Matters
This research holds substantial implications for the future of sustainable and deployable AI, particularly in an era where AI's computational demands are skyrocketing. The ability to achieve nearly 60% energy reduction in CNNs running on hardware accelerators, with minimal accuracy loss, directly addresses the pressing issue of AI's burgeoning carbon footprint and high operational costs. For professionals in the AI space, this translates into tangible benefits: significantly lower power bills for large-scale AI data centers, extended battery life for edge AI devices, and reduced thermal management challenges in embedded systems. Furthermore, it underscores the critical importance of a hardware-aware software optimization paradigm. Moving beyond generic compression techniques, this layer-wise, energy-modeled approach demonstrates that deep integration of architectural insights (like systolic array characteristics) with training methodologies (quantization-aware training) is paramount for unlocking next-generation efficiency. This breakthrough is not just about saving energy; it's about making advanced AI more accessible, sustainable, and economically viable across a broader spectrum of applications, from resource-constrained IoT devices to massive cloud deployments, thereby accelerating the democratization of powerful deep learning models.